|
500.112 (E) |
GATEWAY COMPUTING: JAVA (3) More, Garg This course introduces fundamental programming concepts and techniques, and is intended for all who plan to develop computational artifacts or intelligently deploy computational tools in their studies and careers. Topics covered include the design and implementation of algorithms using variables, control structures, arrays, functions, files, testing, debugging, and structured program design. Elements of object-oriented programming, algorithmic efficiency and data visualization are also introduced. Students deploy programming to develop working solutions that address problems in engineering, science and other areas of contemporary interest that vary from section to section. Course homework involves significant programming. Attendance and participation in class sessions are expected. |
MWF 50 minutes, limit 19/section |
|
601.104 (H) |
COMPUTER ETHICS (1) Leschke
Students will examine a variety of topics regarding policy, legal, and
moral issues related to the computer science profession itself and to the
proliferation of computers in all aspects of society, especially in the era
of the Internet. The course will cover various general issues related to
ethical frameworks and apply those frameworks more specifically to the use
of computers and the Internet. The topics will include privacy issues,
computer crime, intellectual property law -- specifically copyright and
patent issues, globalization, and ethical responsibilities for computer
science professionals. Work in the course will consist of weekly
assignments on one or more of the readings and a final paper on a topic
chosen by the student and approved by the instructor. |
Sec 01: Mon 4:30-6:00p |
|
601.124 (EH) |
THE ETHICS OF ARTIFICIAL INTELLIGENCE & AUTOMATION (3) Lopez-Gonzalez The expansion of artificial intelligence
(AI)-enabled use cases across a broad spectrum of domains has
underscored the benefits and risks of AI. This course will address the
various ethical considerations engineers need to engage with to build
responsible and trustworthy AI-enabled autonomous systems. Topics to
be covered include: values-based decision making, ethically aligned
design, cultural diversity, safety, bias, AI explainability, privacy,
AI regulation, the ethics of synthetic life, and the future of
work. Case studies will be utilized to illustrate real-world
applications. Students will apply learned material to a group research
project on a topic of their choice. |
Sec 01: MW 1:30-2:45p |
|
601.220 (E)
|
INTERMEDIATE PROGRAMMING (4) staff This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover low-level programming techniques, as well as object-oriented class design, and the use of class libraries. Specific topics include pointers, dynamic memory allocation, polymorphism, overloading, inheritance, templates, collections, exceptions, and others as time permits. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages. Prereq: AP CS or (>=C+ grade in one of 500.112, 500.113, 500.114, 580.200) or (500.132 or 500.133 or 500.134) or equivalent by permission. |
CS/CE/EE majors/minors only |
|
601.226 (EQ) |
DATA STRUCTURES (4) Madooei, Presler-Marshall This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prereq: AP CS or (>= C+ grade in 601.220 or 500.112) or 500.132 or equivalent by permission. |
Sec 01 (Presler-Marshall): MWF 1:30-2:45pm, limit 100 |
|
601.229 (E) |
COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer This course covers modern computer systems from a software perspective. Topics include binary data representation, machine arithmetic, assembly language, computer architecture, performance optimization, memory hierarchy and cache organization, virtual memory, Unix systems programming, network programming, and concurrency. Hardware and software interactions relevant to computer security are highlighted. Students will gain hands-on experience with these topics in a series of programming assignments. Prereq: 601.220. |
Sec 01: MWF 9-9:50am, limit 47 |
|
601.230 (EQ) |
MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE (4) Gagan This course provides an introduction to mathematical reasoning and discrete structures relevant to computer science. Topics include propositional and predicate logic, proof techniques including mathematical induction, sets, relations, functions, recurrences, counting techniques, simple computational models, asymptotic analysis, discrete probability, graphs, trees, and number theory. Pre-req: Gateway Computing (500.112/113/114/132/133/134 or AP CS or 601.220). Students can get credit for at most one of EN.601.230 or EN.601.231. |
Sec 01: TuTh 12-1:15p, F 9-9:50a |
|
601.257 (E) |
COMPUTER GRAPHICS & 3D GAME PROGRAMMING (3) Simari In this course, students will program a game of their own design using an off-the-shelf game engine while learning about the 3D computer graphics concepts behind the engine's components. Classes will consist of a mix of theory and practice. The theory will be presented through lectures on topics including transformations, lighting, shading, shape representations, spatial querying and indexing, animation, and special effects. Practice will involve in-class programming exercises and contributions to the game project with periodic in-class presentations of progress to date. Students are expected to have a strong programming background and to be familiar with basic linear algebra concepts. Prereq: 601.220, 601.226 and linear algebra. |
MWF 8a |
|
601.280 (E) |
FULL-STACK JAVASCRIPT (3) Madooei A full-stack JavaScript developer is a person who can build modern software applications using primarily the JavaScript programming language. Creating a modern software application involves integrating many technologies - from creating the user interface to saving information in a database and everything else in between and beyond. A full-stack developer is not an expert in everything. Rather, they are someone who is familiar with various (software application) frameworks and the ability to take a concept and turn it into a finished product. This course will teach you programming in JavaScript and introduce you to several JavaScript frameworks that would enable you to build modern web, cross-platform desktop, and native/hybrid mobile applications. A student who successfully completes this course will be on the expedited path to becoming a full-stack JavaScript developer. Prereq: 601.220 or 601.226. |
TuTh 12-1:15pm |
|
601.315 (E) |
DATABASES (3) Yarowsky Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 601.226. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15pm |
|
660.410 (E) |
CS INNOVATION AND ENTREPRENEURSHIP I (3) Dahbura & Aronhime
[Counts towards "CS other" credits and is a pre-requisite for 601.411
offered in the Spring.] |
MW 12-1:15 |
|
601.414 (E) |
COMPUTER NETWORKS (3) Zhao Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-on programming assignments, homeworks and two exams. Prerequisites: EN.601.226 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
TuTh 1:30-2:45p |
|
601.415 (E) |
DATABASES (3) Yarowsky Similar material as 601.315, covered in more depth, for advanced undergraduates. (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 601.226. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15pm |
|
601.420 (E) |
PARALLEL COMPUTING FOR DATA SCIENCE (3) Burns This course studies parallelism in data science, drawing examples from data analytics, statistical programming, and machine learning. It focuses mostly on the Python programming ecosystem but will use C/C++ to accelerate Python and Java to explore shared-memory threading. It explores parallelism at all levels, including instruction level parallelism (pipelining and vectorization), shared-memory multicore, and distributed computing. Concepts from computer architecture and operating systems will be developed in support of parallelism, including Moore’s law, the memory hierarchy, caching, processes/threads, and concurrency control. The course will cover modern data-parallel programming frameworks, including Dask, Spark, Hadoop!, and Ray. The course will not cover GPU deep-learning frameworks nor CUDA. The course is suitable for second-year undergraduate CS majors and graduate students from other science and engineering disciplines that have prior programming experience.
Required course background: 601.226 and 601.229 or equiv, familiarity with Python. |
MW 4:30-5:45pm |
|
601.421 (E) |
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Darvish This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. Prereq: 601.220, 601.226, and (EN.601.280 or EN.601.290). Students may receive credit for only one of 601.421/621. |
MWF 3-3:50p |
|
601.428 (E) |
COMPILERS & INTERPRETERS (3) Hovemeyer Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. Prereq: 601.226 & 601.229 required; 601.230 or 601.231 recommended |
MW 12-1:15pm |
|
601.429 (E) |
FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith How can we effectively use functional programming techniques to build real-world software? This course will primarily focus on using the OCaml programming language for this purpose. Topics covered include OCaml basics, modules, standard libraries, testing, quickcheck, build tools, functional data structures and efficiency analysis, monads, streams, and promises. Students will practice what they learn in lecture via functional programming assignments and a final project. Pre-req: 601.226 or instructor permission. Students can receive credit for only one of EN.601.429/EN.601.629. |
MW 1:30-2:45pm |
|
601.431 (E,Q) |
THEORY OF COMPUTATION (3) Li This course covers the theoretical foundations of computer science. Topics included will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 601.231 and 601.431/601.631, unless one is for an undergrad degree and the other for grad. Prereq: discrete math or EN.601.230 or permission. Discrete probability recommended. Students can receive credit for only one of EN.601.431/EN.601.631. |
TuTh 1:30-2:45 |
|
601.433 (EQ) |
INTRO ALGORITHMS (3) Dinitz This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as union-find); graph . algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis. Prereq: 601.226 & (553.171/172 or 601.230 or 601.231). Students may receive credit for only one of 601.433/633. |
Sec 01: TuTh 9-10:15am, limit 95 |
|
601.441 (E)
|
BLOCKCHAINS AND CRYPTOCURRENCIES Green This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. Students may receive credit for only one of 600.451, 601.441, 601.641. Prereq: 601.226 and probability (EN.553.211/EN.553.310/EN.553.311/EN.553.420/EN.560.348). |
MW 12-1:15 |
|
601.443 (E) |
SECURITY AND PRIVACY IN COMPUTING (3) Rushanan & Martin Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semester-long project that will be done in teams and will include a presentation by each group to the class. Prerequisite: 601.229. Students may receive credit for only one of 601.443/643. |
TuTh 12-1:15p |
|
601.447 (E) |
COMPUTATIONAL GENOMICS: SEQUENCES (3) Langmead Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, real-world genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming projects. Prereq: 601.220 & 601.226. Students may receive credit for at most one of 601.447/647/747. |
TuTh 9-10:15am |
|
601.449 |
COMPUTATIONAL GENOMICS: APPLIED COMPARATIVE GENOMICS (3) Schatz The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. Prereq: working knowledge of the Unix operating system and programming expertise in R or Python. Students may receive credit for only one of EN.601.449, EN.601.649, EN.601.749. |
MW 3-4:15p |
|
601.454 (E) CSCI-APPLCANCELED |
INTRODUCTION TO AUGMENTED REALITY (3) Martin-Gomez
This course introduces students to the field of Augmented Reality. It
reviews its basic definitions, principles, and applications. The
course explains how fundamentals concepts of computer vision are
applied for the development of Augmented Reality applications. It then
focuses on describing the principal components and particular
requirements to implement a solution using this technology. The course
also discusses the main issues of calibration, tracking, multi-modal
registration, advanced visualization, and display
technologies. Homework in this course will relate to the mathematical
methods used for calibration, tracking, and visualization in augmented
reality.
Prerequisites: EN.601.220, EN.601.226, linear algebra. Students may receive credit for only one of 601.454/654. |
TuTh 3-4:15p |
|
601.455 (E) |
COMPUTER INTEGRATED SURGERY I (4) Taylor This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. (http://www.cisst.org/~cista/445/index.html) Prereq: 601.226 and linear algebra, or permission. Recmd: 601.220, 601.457, 601.461, image processing. Students may earn credit for only one of 601.455/655. |
TuTh 1:30-2:45pm |
|
601.461 (EQ) |
COMPUTER VISION (3) Katyal This course provides an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3D geometry from binocular stereo, motion, and photometric stereo, and object recognition, image segmentation, and activity analysis. Elements of machine vision and biological vision are also included. Prereq: intro programming, linear algebra, prob/stat. Students can earn credit for at most one of 601.461/661/761. |
Tu 4:30-7p |
|
601.463 (E) |
ALGORITHMS FOR SENSOR-BASED ROBOTICS (3) Leonard This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems. Prereq: 601.226, Calc III, linear algebra & probability. Students may receive credit for only one of 601.463/663/763. |
Sec 01: TuTh 3-4:15p |
601.464 (E) |
ARTIFICIAL INTELLIGENCE (3) Haque & Cachola The class is recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as learning, planning and prediction. Materials will be primarily based on the popular textbook, Artificial Intelligence: A Modern Approach. Strong programming skills are expected, as well as basic familiarity with probability. For students intending to also take courses in Machine Learning (e.g., 601.475/675, 601.476/676), they may find it beneficial to take this course first, or concurrently. Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664. |
Sec 01 [Haque]: TuTh 3-4:15p, limit 80, CS/CE majors/minors |
|
601.465 (E) |
NATURAL LANGUAGE PROCESSING (4) Eisner An in-depth introduction to core techniques for analyzing, transforming, and generating human language. The course spans linguistics, modeling, algorithms, and applications. (1) How should linguistic structure and meaning be represented (e.g., trees, morphemes, λ-terms, vectors)? (2) How can we formally model the legal structures and their probabilities (e.g., grammars, automata, features, log-linear models, recurrent neural nets, Transformers)? (3) What algorithms can estimate the parameters of these models (e.g., gradient descent, EM) and efficiently identify probable structures (e.g., dynamic programming, beam search)? (4) Finally, what kinds of systems can be built with these techniques and how are they constructed and evaluated in practice? Detailed assignments guide students through many details of implementing core NLP methods. The course proceeds from first principles, although prior exposure to AI, statistics, ML, or linguistics can be helpful. (www.cs.jhu.edu/~jason/465) Prerequisite: 601.226 and basic familiarity with Python, partial derivatives, matrix multiplication, and probabilities. Students may receive credit for at most one of 601.465/665. |
Lect: MWF 3-4:15 |
|
601.467 (E) |
INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. |
TuTh 9-10:15 |
|
601.468 (E) |
MACHINE TRANSLATION (3) Koehn Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. Required course background: prob/stat, 601.226. Students may receive credit for at most one of 601.468/668. |
TuTh 1:30-2:45 |
|
601.470 (E) |
ARTIFICIAL AGENTS (3) VanDurme This course covers a number of topics explored in introductory AI, such as knowledge representation, reasoning, and natural language understanding. Unlike introductory AI, we will pursue these topics based on the transformer neural architecture. We will motivate the material through interacting with assistive agents: how to build models that understand commands, how to generate responses back to a user, and how to reason about a synthetic environment to determine a course of action. Assignments will include programming, student presentations on readings, written summaries and quizzes on readings, and a final project. Prereq: (Machine Learning, or Machine Learning: Deep Learning, or Machine Translation, or Artificial System Design and Development), or (experience with pytorch or related environment and instructor approval). [601.475/675 OR 601.482/682 OR 601.468/668 OR 601.486/686] Students may receive credit for at most one of 601.470/670. |
MW 8:30-9:45a |
|
601.473 (E) |
COGNITIVE ARTIFICIAL INTELLIGENCE (3) Shu Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment.
Pre-reqs: Prob/Stat & Linear Algebra & Computing
[((EN.553.420 OR EN.553.421) AND (EN.553.430 OR
EN.553.431)) OR (EN.553.211 OR EN.553.310 OR EN.553.311) AND
(AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295) AND (EN.500.112
OR EN.500.113 OR EN.500.114 OR EN.601.220 OR AS.250.205)]. |
TuTh 1:30-2:45p |
|
EN.601.474 (EQ) |
ML: LEARNING THEORY (3) Arora This is a graduate level course in machine learning. It will provide a formal and in-depth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine- learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. Pre-reqs: multivariable calculus (110.202 or 110.211) & probability (553.310/553.311 or 553.420 or 560.348) & linear algebra (110.201 or 110.212 or 553.291) & intro computing (EN.500.112, EN.500.113, EN.500.114, EN.601.220 or AS.250.205). Recommended: prior coursework in ML. Students may receive credit for only one of 601.474/674. |
MWF 12-1:15p |
|
601.475 (E) |
MACHINE LEARNING (3) Shpitser
Machine learning is subfield of computer science and artificial
intelligence, whose goal is to develop computational systems,
methods, and algorithms that can learn from data to improve their
performance. This course introduces the foundational concepts of
modern Machine Learning, including core principles, popular
algorithms and modeling platforms. This will include both supervised
learning, which includes popular algorithms like SVMs, logistic
regression, boosting and deep learning, as well as unsupervised
learning frameworks, which include Expectation Maximization and
graphical models. Homework assignments include a heavy programming
components, requiring students to implement several machine learning
algorithms in a common learning framework. Additionally, analytical
homework questions will explore various machine learning concepts,
building on the pre-requisites that include probability, linear
algebra, multi-variate calculus and basic optimization. Students in
the course will develop a learning system for a final project.
Pre-reqs: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Students may receive credit for only one of 601.475/675. |
MWF 12-1:15p |
|
601.482 (E) |
MACHINE LEARNING: DEEP LEARNING (4) Unberath
Deep learning (DL) has emerged as a powerful tool for solving
data-intensive learning problems such as supervised learning for
classification or regression, dimensionality reduction, and control. As
such, it has a broad range of applications including speech and text
understanding, computer vision, medical imaging, and perception-based
robotics. Pre-req: Data Structures, Linear Algebra, Probability, Calc II required; Statistics, Machine Learning, Calc III, numerical optimization and Python strongly recommended. Students can receive credit for EN.601.482 or EN.601.682, but not both. |
MW 4:30-5:45p, F 4:30-5:20p |
|
601.485 (Q) |
PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille [Co-listed as AS.050.375/AS.050.675/EN.601.685.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks.
Pre-requisites: Calc I, programming experience (Python
preferred). Students can receive credit for at most one of
EN.601.485/EN.601.685/AS.050.375/AS.050.675.
|
TuTh 9-10:15 |
|
601.486 (E) |
MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT (3) Dredze
Advances in Artificial intelligence have opened new opportunities for
developing systems to aid in numerous areas of society. In order for
AI systems to succeed in making constructive and positive changes, we
must consider their impact on everyday life. Specifically, AI system
designers must evaluate the overall capabilities of the system,
consider the resulting human-AI interactions, and ensure that the
system behaves in a responsible and ethical manner. In this
project-based course you will work in teams of 3-5 students to 1)
Identify a need with high-impact implications on everyday life; 2)
Articulate principles of Responsible AI relevant to the intended
application, 3) Conceptualize and design an AI system targeting this
need, and 4) Develop the AI system by refining a demo-able prototype
based on feedback received during course presentations. Additionally,
we will discuss potential ethical issues that can arise in AI and how
to develop Responsible AI principles. Coursework will consist of
writing assignments, project presentations, and a project
demonstration.
Pre-req: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design). |
MW 1:30-2:45 |
|
601.489 (E) |
HUMAN-IN-THE-LOOP MACHINE LEARNING (3) Nalisnick
Machine learning (ML) is being deployed in increasingly consequential
tasks, such as healthcare and autonomous driving. For the foreseeable
future, successfully deploying ML in such settings will require close
collaboration and integration with humans, whether they be users,
designers, engineers, policy-makers, etc. This course will look at how
humans can be incorporated into the foundations of ML in a principled
way. The course will be broken down into three parts: demonstration,
collaboration, and oversight. Demonstration is about how machines can
learn from 'observing' humans---such as learning to drive a car from
data collected while humans drive. In this setting, the human is
assumed to be strictly better than the machine and so the primary goal
is to transmit the human's knowledge and abilities into the ML
model. The second part, collaboration, is about when humans and models
are near equals in performance but not in abilities. A relevant
setting is AI-assisted healthcare: perhaps a human radiologist and ML
model are good at diagnosing different kinds of diseases. Thus we will
look at methodologies that allow machines to ‘ask for help' when they
are either unconfident in their own performance and/or think the human
can better carry out the task. The course will close with the setting
in which machines are strictly better at a task than humans are, but
we still wish to monitor them to ensure safety and alignment with our
goals (oversight). Assessment will be done with homework, quizzes,
and a final project. Pre-req: EN.601.475/675. |
MW 1:30-2:45 |
|
601.490 (E) |
INTRO TO HUMAN-COMPUTER INTERACTION (3) Xiao & Reiter
This course is designed to introduce undergraduate and graduate
students to design techniques and practices in human-computer
interaction (HCI), the study of interactions between humans and
computing systems. Students will learn design techniques and
evaluation methods, as well as current practices and exploratory
approaches, in HCI through lectures, readings, and
assignments. Students will practice various design techniques and
evaluation methods through hands-on projects focusing on different
computing technologies and application domains. This course is
intended for undergraduate and graduate students in Computer
Science/Cognitive Science/Psychology. Interested students from
different disciplines should contact the instructor before enrolling
in this course. Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01 (Xiao): TuTh 3-4:15, limit 20, CS majors/minors |
|
601.501 |
COMPUTER SCIENCE WORKSHOP An applications-oriented, computer science project done under the supervision and with the sponsorship of a faculty member in the Department of Computer Science. Computer Science Workshop provides a student with an opportunity to apply theory and concepts of computer science to a significant project of mutual interest to the student and a Computer Science faculty member. Permission to enroll in CSW is granted by the faculty sponsor after his/her approval of a project proposal from the student. Interested students are advised to consult with Computer Science faculty members before preparing a Computer Science Workshop project proposal. Perm. of faculty supervisor req'd. |
See below for faculty section numbers |
|
601.503 |
INDEPENDENT STUDY Individual, guided study for undergraduate students under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See below for faculty section numbers |
|
601.507 |
UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See below for faculty section numbers and whether to select 507 or 517. |
|
601.509 |
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study and credit assigned must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit. S/U only. Permission required. |
See below for faculty section numbers |
|
601.513 |
GROUP UNDERGRADUATE PROJECT Independent learning and application for undergraduates under the direction of a faculty member in the department. This course has a regular project group meeting that students are expected to attend. The individual project contributions, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
Only for faculty specifically marked below. |
|
601.517 |
GROUP UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
Only for faculty specifically marked below. |
|
601.519 |
SENIOR HONORS THESIS (3) For computer science majors only. The student will undertake a substantial independent research project under the supervision of a faculty member, potentially leading to the notation "Departmental Honors with Thesis" on the final transcript. Students are expected to enroll in both semesters of this course during their senior year. Project proposals must be submitted and accepted in the preceding spring semester (junior year) before registration. Students will present their work publically before April 1st of senior year. They will also submit a first draft of their project report (thesis document) at that time. Faculty will meet to decide if the thesis will be accepted for honors. Prereq: 3.5 GPA in Computer Science after spring of junior year and permission of faculty supervisor. |
See below for faculty section numbers |
|
601.556 |
SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3) The student will undertake a substantial independent research project in the area of computer-integrated surgery, under joint supervision of a WSE faculty adviser and a clinician or clinical researcher at the Johns Hopkins Medical School. Prereq: 601.455 or perm req'd. |
Section 1: Taylor |
|
601.614 |
COMPUTER NETWORKS (3) Zhao Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-on programming assignments, homeworks and two exams. Required Course Background: C/C++ programming and data structures, or permission. Students can only receive credit for one of 601.414/614. |
TuTh 1:30 or 3-4:15p |
|
601.615 |
DATABASES Yarowsky Same material as 601.415, for graduate students. (www.cs.jhu.edu/~yarowsky/cs415.html) Required course background: Data Structures. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15 |
|
601.620 |
PARALLEL COMPUTING FOR DATA SCIENCE (3) Burns This course studies parallelism in data science, drawing examples from data analytics, statistical programming, and machine learning. It focuses mostly on the Python programming ecosystem but will use C/C++ to accelerate Python and Java to explore shared-memory threading. It explores parallelism at all levels, including instruction level parallelism (pipelining and vectorization), shared-memory multicore, and distributed computing. Concepts from computer architecture and operating systems will be developed in support of parallelism, including Moore’s law, the memory hierarchy, caching, processes/threads, and concurrency control. The course will cover modern data-parallel programming frameworks, including Dask, Spark, Hadoop!, and Ray. The course will not cover GPU deep-learning frameworks nor CUDA. The course is suitable for second-year undergraduate CS majors and graduate students from other science and engineering disciplines that have prior programming experience.
Required course background: 601.226 and 601.229 or equiv, familiarity with Python. |
MW 4:30-5:45pm |
|
601.621 |
OBJECT ORIENTED SOFTWARE ENGINEERING Darvish Same material as EN.601.421, for graduate students. This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. Required course background: intermediate programming, data structures, and experience in mobile or web app development. Students may receive credit for only one of 601.421/621. |
MWF 3-3:50p |
|
601.628 |
COMPILERS & INTERPRETERS Hovemeyer Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. Expected course background: 601.220, 601.226 & 601.229 required; 601.230 or 601.231 recommended |
MW 12-1:15 |
|
601.629 |
FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith
How can we effectively use functional programming techniques to build
real-world software? This course will primarily focus on using the
OCaml programming language for this purpose. Topics covered include
OCaml basics, modules, standard libraries, testing, quickcheck, build
tools, functional data structures and efficiency analysis, monads,
streams, and promises. Students will practice what they learn in
lecture via functional programming assignments and a final
project. |
MW 1:30-2:45pm |
|
601.631 |
THEORY OF COMPUTATION (3) Li This course covers the theoretical foundations of computer science. Topics included will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 601.231 and 601.431/601.631, unless one is for an undergrad degree and the other for grad. Required Background: discrete math or permission; discrete probability theory recommended. Students can receive credit for only one of EN.601.431/EN.601.631. |
TuTh 1:30-2:45 |
|
601.633 |
INTRO ALGORITHMS Dinitz Same material as 601.433, for graduate students. Required Background: Data Structures and (Discrete Math or Automata/Computation Theory). Students may receive credit for only one of 601.433/633. |
TuTh 9-10:15a |
|
601.641 |
BLOCKCHAINS AND CRYPTOCURRENCIES Green [Cross-listed in JHUISI.] Same as EN.601.441, for graduate students. Students may receive credit for only one of 600.451, 601.441, 601.641. Required course background: 601.226 and probability (any course). |
MW 12-1:15 |
|
601.643 |
SECURITY AND PRIVACY IN COMPUTING Rushanan & Martin Same material as 601.443, for graduate students. Required course background: C programming and computer system fundamentals. |
TuTh 12-1:15p |
|
601.647 |
COMPUTATIONAL GENOMICS: SEQUENCES Langmead Same material as 601.447, for graduate students. Required Course Background: Intermediate Programming (C/C++) and Data Structures. Students may earn credit for at most one of 601.447/647/747. |
TuTh 9-10:15 |
|
601.649 |
COMPUTATIONAL GENOMICS: APPLIED COMPARATIVE GENOMICS (3) Schatz [Formerly EN.601.749.] The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. Prereq: Prereq: working knowledge of the Unix operating system and programming expertise in R or Python. Students may receive credit for only one of EN.601.449, EN.601.649, EN.601.749. |
MW 3-4:15p |
|
601.654 |
INTRODUCTION TO AUGMENTED REALITY (3) Martin-Gomez
This course introduces students to the field of Augmented Reality. It
reviews its basic definitions, principles, and applications. The
course explains how fundamentals concepts of computer vision are
applied for the development of Augmented Reality applications. It then
focuses on describing the principal components and particular
requirements to implement a solution using this technology. The course
also discusses the main issues of calibration, tracking, multi-modal
registration, advanced visualization, and display
technologies. Homework in this course will relate to the mathematical
methods used for calibration, tracking, and visualization in augmented
reality.
Required course background: intermediate programming (C/C++), data structures, linear algebra. Students may receive credit for only one of 601.454 or 601.654, but not both. |
TuTh 3-4:15p |
|
601.655 |
COMPUTER INTEGRATED SURGERY I Taylor Same material as 601.455, for graduate students. (http://www.cisst.org/~cista/445/index.html) Prereq: data structures and linear algebra, or permission. Recommended: intermediate programming in C/C++, computer graphics, computer vision, image processing. Students may earn credit for 601.455 or 601.655, but not both. |
TuTh 1:30-2:45 |
|
601.661 |
COMPUTER VISION Katyal Same material as 601.461, for graduate students. Students may receive credit for at most one of 601.461/661/761. (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Required course background: intro programming & linear algebra & prob/stat |
Tu 4:30-7p |
|
601.663 |
ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard Same material as EN.601.463, for graduate students. Required course background: data structures, Calc III, linear algebra & prob/stat. Students may receive credit for only one of 601.463/663/763. |
Sec 01: TuTh 3-4:15p, limit 30, WSE + Non-ASEN grads |
|
601.664 |
ARTIFICIAL INTELLIGENCE Haque & Cachola Same as 601.464, for graduate students. Prereq: Data Structures; Recommended: linear algebra & prob/stat. Students can only receive credit for one of 601.464/664. |
Sec 01 [Haque]: TuTh 3-4:15p, limit 30, CS + MSEM grads |
|
601.665 |
NATURAL LANGUAGE PROCESSING Eisner Same material as 601.465, for graduate students. (www.cs.jhu.edu/~jason/465) Prerequisite: data structures and basic familiarity with Python, partial derivatives, matrix multiplication, and probabilities. Students may receive credit for at most one of 601.465/665. |
Lect: MWF 3-4:15 |
|
601.667 (E) |
INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. |
TuTh 9-10:15 |
|
601.668 |
MACHINE TRANSLATION Koehn Same material as 601.468, for graduate students. Required course background: prob/stat, data structures. Student may receive credit for at most one of 601.468/668. |
TuTh 1:30-2:45 |
|
601.670 |
ARTIFICIAL AGENTS (3) VanDurme This course covers a number of topics explored in introductory AI, such as knowledge representation, reasoning, and natural language understanding. Unlike introductory AI, we will pursue these topics based on the transformer neural architecture. We will motivate the material through interacting with assistive agents: how to build models that understand commands, how to generate responses back to a user, and how to reason about a synthetic environment to determine a course of action. Assignments will include programming, student presentations on readings, written summaries and quizzes on readings, and a final project. Required Course Background: (Machine Learning, or Machine Learning: Deep Learning, or Machine Translation, or Artificial System Design and Development), or (experience with pytorch or related environment and instructor approval). [601.475/675 OR 601.482/682 OR 601.468/668 OR 601.486/686] Students may receive credit for at most one of 601.470/670. |
MW 8:30-9:45a |
|
601.673 (E) |
COGNITIVE ARTIFICIAL INTELLIGENCE (3) Shu Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment.
Required Course Background: Prob/Stat & Linear Algebra &
Computing; prior course in ML/AI strongly recommended.
|
TuTh 1:30-2:45p |
|
EN.601.674 |
ML: LEARNING THEORY Arora [Formerly: Statistical Machine Learning] This is a graduate level course in machine learning. It will provide a formal and in-depth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine- learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. Required course background: multivariable calculus, probability, linear algebra, intro computing. Recommended: prior coursework in ML. Students may receive credit for only one of 601.474/674. |
MWF 12-1:15p |
|
601.675 |
MACHINE LEARNING Shpitser
Same material as 601.475, for graduate students.
Required course background: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Student may receive credit for only one of 601.475/675. |
MWF 12-1:15p |
|
601.682 |
MACHINE LEARNING: DEEP LEARNING Unberath
Same as 601.482, for graduate students.
Required course background: Data Structures, Linear Algebra, Probability, Calc II required; Statistics, Machine Learning, Calc III, numerical optimization and Python strongly recommended. Students may receive credit for 601.482 or 601.682 but not both. |
MW 4:30-5:45p, F 4:30-5:20p |
|
EN.601.685 |
PROBABILISTIC MODELS OF THE VISUAL CORTEX Yuille [Co-listed as AS.050.375/AS.050.675/EN.601.485.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. Pre-requisites: Calc I, programming experience (Python
preferred). Students can receive credit for at most one of
EN.601.485/EN.601.685/AS.050.375/AS.050.675.
|
TuTh 9-10:15 |
|
601.686 |
MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT Dredze
Advances in Artificial intelligence have opened new opportunities for
developing systems to aid in numerous areas of society. In order for
AI systems to succeed in making constructive and positive changes, we
must consider their impact on everyday life. Specifically, AI system
designers must evaluate the overall capabilities of the system,
consider the resulting human-AI interactions, and ensure that the
system behaves in a responsible and ethical manner. In this
project-based course you will work in teams of 3-5 students to 1)
Identify a need with high-impact implications on everyday life; 2)
Articulate principles of Responsible AI relevant to the intended
application, 3) Conceptualize and design an AI system targeting this
need, and 4) Develop the AI system by refining a demo-able prototype
based on feedback received during course presentations. Additionally,
we will discuss potential ethical issues that can arise in AI and how
to develop Responsible AI principles. Coursework will consist of
writing assignments, project presentations, and a project
demonstration.
Required course background: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design). |
MW 1:30-2:45 |
|
601.689 |
HUMAN-IN-THE-LOOP MACHINE LEARNING (3) Nalisnick
Machine learning (ML) is being deployed in increasingly consequential
tasks, such as healthcare and autonomous driving. For the foreseeable
future, successfully deploying ML in such settings will require close
collaboration and integration with humans, whether they be users,
designers, engineers, policy-makers, etc. This course will look at how
humans can be incorporated into the foundations of ML in a principled
way. The course will be broken down into three parts: demonstration,
collaboration, and oversight. Demonstration is about how machines can
learn from 'observing' humans---such as learning to drive a car from
data collected while humans drive. In this setting, the human is
assumed to be strictly better than the machine and so the primary goal
is to transmit the human's knowledge and abilities into the ML
model. The second part, collaboration, is about when humans and models
are near equals in performance but not in abilities. A relevant
setting is AI-assisted healthcare: perhaps a human radiologist and ML
model are good at diagnosing different kinds of diseases. Thus we will
look at methodologies that allow machines to ‘ask for help' when they
are either unconfident in their own performance and/or think the human
can better carry out the task. The course will close with the setting
in which machines are strictly better at a task than humans are, but
we still wish to monitor them to ensure safety and alignment with our
goals (oversight). Assessment will be done with homework, quizzes,
and a final project. Prerequisite: EN.601.475/675 or equivalent. |
MW 1:30-2:45 |
|
601.690 |
INTRO TO HUMAN-COMPUTER INTERACTION Xiao & Reiter
Same material as EN.601.490, for graduate students. Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01: TuTh 3-4:15, limit 15, CS only |
|
601.713 |
FUTURE NETWORKS Sabnani
This will be a graduate-level networking course. New applications
such as ones for metaverse require networking and computing to be
imbedded together. This feature is already beginning to be
implemented in 5G and 6G networks; 6G will also allow networks to
be used as sensors. These advances are enabled by new technologies
such as mobile edge computing, software-defined networking (SDN),
network slicing, digital twins, and named-data networking
(NDN). This course will start with introductory lectures on these
topics. Students will be asked to study new papers and do course
projects. These activities should result in longer term research
projects. |
Tu 4:30-7p |
|
601.723 CANCELLED |
ADVANCED TOPICS IN PARALLEL COMPUTING FOR DATA SCIENCE Burns This course will study recent advances in the systems support for scalable machine learning and artificial intelligence workloads. We will look at recent advances in programming languages, compilers, and operating systems and how they are being implemented in hardware accelerators. Topics will include data sparsity, single-instruction multi-threading, vectorization of memory and processing, and scatter/gather/reduce patterns. The course will alternate between lectures and seminar-style paper discussions. Each student will be expected to conduct and present a substantial research project individually or in teams. Required Course Background: 601.420/620 or equivalent. |
|
|
601.770 |
AI ETHICS AND SOCIAL IMPACT Field
AI is poised to have an enormous impact on society. What should that
impact be and who should get to decide it? The goal of this course
is to critically examine AI research and deployment pipelines,
with in-depth examinations of how we need to understand social
structures to understand impact. In application domains, we will
examine questions like “who are key stakeholders?”, “who is
affected by this technology?” and “who benefits from this
technology?”. We will also conversely examine: how can AI help us
learn about these domains, and can we build from this knowledge to
design AI for "social good"? As a graduate-level course, topics
will focus on current research including development and
deployment of technologies like large language models and decision
support tools, and students will conduct a final research project.
|
TuTh 12-1:15p |
|
601.771 |
ADVANCES IN SELF-SUPERVISED STATISTICAL MODELS Khashabi
The rise of massive self-supervised (pre-trained) models has
transformed various data-driven fields such as natural language
processing, computer vision, robotics, and medical imaging. This
advanced graduate course aims to provide a holistic view of the issues
related to these models: We will start with the history of how we got
here, and then delve into the latest success stories. We will then
focus on the implications of these technologies: social harms,
security risks, legal issues, and environmental impacts. The class
ends with reflections on the future implications of this trajectory.
|
TuTh 9-10:15a |
|
601.787 |
ADVANCED MACHINE LEARNING: MACHINE LEARNING FOR TRUSTWORTHY AI Liu This course teaches advanced machine learning methods for the design, implementation, and deployment of trustworthy AI systems. The topics we will cover include but are not limit to different types of robust learning methods, fair learning methods, safe learning methods, and research frontiers in transparency, interpretability, privacy, sustainability, AI safety and ethics. Students will learn the state-of-the-art methods in lectures, understand the recent advances by critiquing research articles, and apply/innovate new machine learning methods in an application. There will be homework assignments and a course project. Expected course background: 601.475/675 Machine Learning; recommended 601.476/676 ML: Data to Models and 601.482/682 Deep Learning. |
MW 3-4:15 |
|
601.788 |
MACHINE LEARNING FOR HEALTHCARE Oberst This course surveys the technical and practical challenges of applying machine learning in healthcare, focusing on two themes: The first theme will cover applications of machine learning to a wide range of healthcare data modalities (e.g., medical imaging, structured health records, etc). Beyond reviewing specific modeling approaches, we will focus on navigating pitfalls in model development and evaluation that arise in a healthcare context. The second theme will cover methodological approaches to developing safe and effective machine learning systems in healthcare, including topics such as (but not limited to) causality, fairness, and distribution shift. This course is designed for students who have a solid existing background in machine learning, and who are interested in both the technical and practical nuances of applying machine learning in healthcare. Grading will be done on the basis of homework assignments as well as a final project. Required course background: 601.475/675 Machine Learning. |
TuTh 9-10:15a |
|
601.104 (H) |
COMPUTER ETHICS (1) Leschke
Students will examine a variety of topics regarding policy, legal, and
moral issues related to the computer science profession itself and to the
proliferation of computers in all aspects of society, especially in the era
of the Internet. The course will cover various general issues related to
ethical frameworks and apply those frameworks more specifically to the use
of computers and the Internet. The topics will include privacy issues,
computer crime, intellectual property law -- specifically copyright and
patent issues, globalization, and ethical responsibilities for computer
science professionals. Work in the course will consist of weekly
assignments on one or more of the readings and a final paper on a topic
chosen by the student and approved by the instructor. |
Sec 01: Mon 4:30-6:00p |
|
601.124 (EH) |
THE ETHICS OF ARTIFICIAL INTELLIGENCE & AUTOMATION (3) Lopez-Gonzalez The expansion of artificial intelligence
(AI)-enabled use cases across a broad spectrum of domains has
underscored the benefits and risks of AI. This course will address the
various ethical considerations engineers need to engage with to build
responsible and trustworthy AI-enabled autonomous systems. Topics to
be covered include: values-based decision making, ethically aligned
design, cultural diversity, safety, bias, AI explainability, privacy,
AI regulation, the ethics of synthetic life, and the future of
work. Case studies will be utilized to illustrate real-world
applications. Students will apply learned material to a group research
project on a topic of their choice. |
Sec 01: MW 1:30-2:45p |
|
601.220 (E)
|
INTERMEDIATE PROGRAMMING (4) staff This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover low-level programming techniques, as well as object-oriented class design, and the use of class libraries. Specific topics include pointers, dynamic memory allocation, polymorphism, overloading, inheritance, templates, collections, exceptions, and others as time permits. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages. Prereq: AP CS or (>=C+ grade in one of 500.112, 500.113, 500.114, 580.200) or (500.132 or 500.133 or 500.134) or equivalent by permission. |
CS/CE/EE majors/minors only |
|
601.226 (EQ) |
DATA STRUCTURES (4) Madooei, Presler-Marshall This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prereq: AP CS or (>= C+ grade in 601.220 or 500.112) or 500.132 or equivalent by permission. |
Sec 01 (Presler-Marshall): MWF 1:30-2:45pm, limit 100 |
|
601.229 (E) |
COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer This course covers modern computer systems from a software perspective. Topics include binary data representation, machine arithmetic, assembly language, computer architecture, performance optimization, memory hierarchy and cache organization, virtual memory, Unix systems programming, network programming, and concurrency. Hardware and software interactions relevant to computer security are highlighted. Students will gain hands-on experience with these topics in a series of programming assignments. Prereq: 601.220. |
Sec 01: MWF 9-9:50am, limit 47 |
|
601.230 (EQ) |
MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE (4) Gagan This course provides an introduction to mathematical reasoning and discrete structures relevant to computer science. Topics include propositional and predicate logic, proof techniques including mathematical induction, sets, relations, functions, recurrences, counting techniques, simple computational models, asymptotic analysis, discrete probability, graphs, trees, and number theory. Pre-req: Gateway Computing (500.112/113/114/132/133/134 or AP CS or 601.220). Students can get credit for at most one of EN.601.230 or EN.601.231. |
Sec 01: TuTh 12-1:15p, F 9-9:50a |
|
601.257 (E) |
COMPUTER GRAPHICS & 3D GAME PROGRAMMING (3) Simari In this course, students will program a game of their own design using an off-the-shelf game engine while learning about the 3D computer graphics concepts behind the engine's components. Classes will consist of a mix of theory and practice. The theory will be presented through lectures on topics including transformations, lighting, shading, shape representations, spatial querying and indexing, animation, and special effects. Practice will involve in-class programming exercises and contributions to the game project with periodic in-class presentations of progress to date. Students are expected to have a strong programming background and to be familiar with basic linear algebra concepts. Prereq: 601.220, 601.226 and linear algebra. |
MWF 8a |
|
601.280 (E) |
FULL-STACK JAVASCRIPT (3) Madooei A full-stack JavaScript developer is a person who can build modern software applications using primarily the JavaScript programming language. Creating a modern software application involves integrating many technologies - from creating the user interface to saving information in a database and everything else in between and beyond. A full-stack developer is not an expert in everything. Rather, they are someone who is familiar with various (software application) frameworks and the ability to take a concept and turn it into a finished product. This course will teach you programming in JavaScript and introduce you to several JavaScript frameworks that would enable you to build modern web, cross-platform desktop, and native/hybrid mobile applications. A student who successfully completes this course will be on the expedited path to becoming a full-stack JavaScript developer. Prereq: 601.220 or 601.226. |
TuTh 12-1:15pm |
|
601.315 (E) |
DATABASES (3) Yarowsky Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 601.226. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15pm |
|
660.410 (E) |
CS INNOVATION AND ENTREPRENEURSHIP I (3) Dahbura & Aronhime
[Counts towards "CS other" credits and is a pre-requisite for 601.411
offered in the Spring.] |
MW 12-1:15 |
|
601.414 (E) |
COMPUTER NETWORKS (3) Zhao Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-on programming assignments, homeworks and two exams. Prerequisites: EN.601.226 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
TuTh 1:30-2:45p |
|
601.415 (E) |
DATABASES (3) Yarowsky Similar material as 601.315, covered in more depth, for advanced undergraduates. (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 601.226. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15pm |
|
601.420 (E) |
PARALLEL COMPUTING FOR DATA SCIENCE (3) Burns This course studies parallelism in data science, drawing examples from data analytics, statistical programming, and machine learning. It focuses mostly on the Python programming ecosystem but will use C/C++ to accelerate Python and Java to explore shared-memory threading. It explores parallelism at all levels, including instruction level parallelism (pipelining and vectorization), shared-memory multicore, and distributed computing. Concepts from computer architecture and operating systems will be developed in support of parallelism, including Moore’s law, the memory hierarchy, caching, processes/threads, and concurrency control. The course will cover modern data-parallel programming frameworks, including Dask, Spark, Hadoop!, and Ray. The course will not cover GPU deep-learning frameworks nor CUDA. The course is suitable for second-year undergraduate CS majors and graduate students from other science and engineering disciplines that have prior programming experience.
Required course background: 601.226 and 601.229 or equiv, familiarity with Python. |
MW 4:30-5:45pm |
|
601.421 (E) |
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Darvish This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. Prereq: 601.220, 601.226, and (EN.601.280 or EN.601.290). Students may receive credit for only one of 601.421/621. |
MWF 3-3:50p |
|
601.428 (E) |
COMPILERS & INTERPRETERS (3) Hovemeyer Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. Prereq: 601.226 & 601.229 required; 601.230 or 601.231 recommended |
MW 12-1:15pm |
|
601.429 (E) |
FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith How can we effectively use functional programming techniques to build real-world software? This course will primarily focus on using the OCaml programming language for this purpose. Topics covered include OCaml basics, modules, standard libraries, testing, quickcheck, build tools, functional data structures and efficiency analysis, monads, streams, and promises. Students will practice what they learn in lecture via functional programming assignments and a final project. Pre-req: 601.226 or instructor permission. Students can receive credit for only one of EN.601.429/EN.601.629. |
MW 1:30-2:45pm |
|
601.431 (E,Q) |
THEORY OF COMPUTATION (3) Li This course covers the theoretical foundations of computer science. Topics included will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 601.231 and 601.431/601.631, unless one is for an undergrad degree and the other for grad. Prereq: discrete math or EN.601.230 or permission. Discrete probability recommended. Students can receive credit for only one of EN.601.431/EN.601.631. |
TuTh 1:30-2:45 |
|
601.433 (EQ) |
INTRO ALGORITHMS (3) Dinitz This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as union-find); graph . algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis. Prereq: 601.226 & (553.171/172 or 601.230 or 601.231). Students may receive credit for only one of 601.433/633. |
Sec 01: TuTh 9-10:15am, limit 80 |
|
601.441 (E)
|
BLOCKCHAINS AND CRYPTOCURRENCIES Green This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. Students may receive credit for only one of 600.451, 601.441, 601.641. Prereq: 601.226 and probability (EN.553.211/EN.553.310/EN.553.311/EN.553.420/EN.560.348). |
MW 12-1:15 |
|
601.443 (E) |
SECURITY AND PRIVACY IN COMPUTING (3) Rushanan & Martin Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semester-long project that will be done in teams and will include a presentation by each group to the class. Prerequisite: 601.229. Students may receive credit for only one of 601.443/643. |
TuTh 12-1:15p |
|
601.447 (E) |
COMPUTATIONAL GENOMICS: SEQUENCES (3) Langmead Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, real-world genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming projects. Prereq: 601.220 & 601.226. Students may receive credit for at most one of 601.447/647/747. |
TuTh 9-10:15am |
|
601.449 |
COMPUTATIONAL GENOMICS: APPLIED COMPARATIVE GENOMICS (3) Schatz The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. Prereq: working knowledge of the Unix operating system and programming expertise in R or Python. Students may receive credit for only one of EN.601.449, EN.601.649, EN.601.749. |
MW 3-4:15p |
|
601.454 (E) CSCI-APPLCANCELED |
INTRODUCTION TO AUGMENTED REALITY (3) Martin-Gomez
This course introduces students to the field of Augmented Reality. It
reviews its basic definitions, principles, and applications. The
course explains how fundamentals concepts of computer vision are
applied for the development of Augmented Reality applications. It then
focuses on describing the principal components and particular
requirements to implement a solution using this technology. The course
also discusses the main issues of calibration, tracking, multi-modal
registration, advanced visualization, and display
technologies. Homework in this course will relate to the mathematical
methods used for calibration, tracking, and visualization in augmented
reality.
Prerequisites: EN.601.220, EN.601.226, linear algebra. Students may receive credit for only one of 601.454/654. |
TuTh 3-4:15p |
|
601.455 (E) |
COMPUTER INTEGRATED SURGERY I (4) Taylor This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. (http://www.cisst.org/~cista/445/index.html) Prereq: 601.226 and linear algebra, or permission. Recmd: 601.220, 601.457, 601.461, image processing. Students may earn credit for only one of 601.455/655. |
TuTh 1:30-2:45pm |
|
601.461 (EQ) |
COMPUTER VISION (3) Katyal This course provides an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3D geometry from binocular stereo, motion, and photometric stereo, and object recognition, image segmentation, and activity analysis. Elements of machine vision and biological vision are also included. Prereq: intro programming, linear algebra, prob/stat. Students can earn credit for at most one of 601.461/661/761. |
Tu 4:30-7p |
|
601.463 (E) |
ALGORITHMS FOR SENSOR-BASED ROBOTICS (3) Leonard This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems. Prereq: 601.226, Calc III, linear algebra & probability. Students may receive credit for only one of 601.463/663/763. |
Sec 01: TuTh 3-4:15p |
601.464 (E) |
ARTIFICIAL INTELLIGENCE (3) Haque & Cachola The class is recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as learning, planning and prediction. Materials will be primarily based on the popular textbook, Artificial Intelligence: A Modern Approach. Strong programming skills are expected, as well as basic familiarity with probability. For students intending to also take courses in Machine Learning (e.g., 601.475/675, 601.476/676), they may find it beneficial to take this course first, or concurrently. Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664. |
Sec 01 [Haque]: TuTh 3-4:15p, limit 80, CS/CE majors/minors |
|
601.465 (E) |
NATURAL LANGUAGE PROCESSING (4) Eisner An in-depth introduction to core techniques for analyzing, transforming, and generating human language. The course spans linguistics, modeling, algorithms, and applications. (1) How should linguistic structure and meaning be represented (e.g., trees, morphemes, λ-terms, vectors)? (2) How can we formally model the legal structures and their probabilities (e.g., grammars, automata, features, log-linear models, recurrent neural nets, Transformers)? (3) What algorithms can estimate the parameters of these models (e.g., gradient descent, EM) and efficiently identify probable structures (e.g., dynamic programming, beam search)? (4) Finally, what kinds of systems can be built with these techniques and how are they constructed and evaluated in practice? Detailed assignments guide students through many details of implementing core NLP methods. The course proceeds from first principles, although prior exposure to AI, statistics, ML, or linguistics can be helpful. (www.cs.jhu.edu/~jason/465) Prerequisite: 601.226 and basic familiarity with Python, partial derivatives, matrix multiplication, and probabilities. Students may receive credit for at most one of 601.465/665. |
Lect: MWF 3-4:15 |
|
601.467 (E) |
INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. |
TuTh 9-10:15 |
|
601.468 (E) |
MACHINE TRANSLATION (3) Koehn Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. Required course background: prob/stat, 601.226. Students may receive credit for at most one of 601.468/668. |
TuTh 1:30-2:45 |
|
601.470 (E) |
ARTIFICIAL AGENTS (3) VanDurme This course covers a number of topics explored in introductory AI, such as knowledge representation, reasoning, and natural language understanding. Unlike introductory AI, we will pursue these topics based on the transformer neural architecture. We will motivate the material through interacting with assistive agents: how to build models that understand commands, how to generate responses back to a user, and how to reason about a synthetic environment to determine a course of action. Assignments will include programming, student presentations on readings, written summaries and quizzes on readings, and a final project. Prereq: (Machine Learning, or Machine Learning: Deep Learning, or Machine Translation, or Artificial System Design and Development), or (experience with pytorch or related environment and instructor approval). [601.475/675 OR 601.482/682 OR 601.468/668 OR 601.486/686] Students may receive credit for at most one of 601.470/670. |
MW 8:30-9:45a |
|
601.473 (E) |
COGNITIVE ARTIFICIAL INTELLIGENCE (3) Shu Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment.
Pre-reqs: Prob/Stat & Linear Algebra & Computing
[((EN.553.420 OR EN.553.421) AND (EN.553.430 OR
EN.553.431)) OR (EN.553.211 OR EN.553.310 OR EN.553.311) AND
(AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295) AND (EN.500.112
OR EN.500.113 OR EN.500.114 OR EN.601.220 OR AS.250.205)]. |
TuTh 1:30-2:45p |
|
EN.601.474 (EQ) |
ML: LEARNING THEORY (3) Arora This is a graduate level course in machine learning. It will provide a formal and in-depth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine- learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. Pre-reqs: multivariable calculus (110.202 or 110.211) & probability (553.310/553.311 or 553.420 or 560.348) & linear algebra (110.201 or 110.212 or 553.291) & intro computing (EN.500.112, EN.500.113, EN.500.114, EN.601.220 or AS.250.205). Recommended: prior coursework in ML. Students may receive credit for only one of 601.474/674. |
MWF 12-1:15p |
|
601.475 (E) |
MACHINE LEARNING (3) Shpitser
Machine learning is subfield of computer science and artificial
intelligence, whose goal is to develop computational systems,
methods, and algorithms that can learn from data to improve their
performance. This course introduces the foundational concepts of
modern Machine Learning, including core principles, popular
algorithms and modeling platforms. This will include both supervised
learning, which includes popular algorithms like SVMs, logistic
regression, boosting and deep learning, as well as unsupervised
learning frameworks, which include Expectation Maximization and
graphical models. Homework assignments include a heavy programming
components, requiring students to implement several machine learning
algorithms in a common learning framework. Additionally, analytical
homework questions will explore various machine learning concepts,
building on the pre-requisites that include probability, linear
algebra, multi-variate calculus and basic optimization. Students in
the course will develop a learning system for a final project.
Pre-reqs: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Students may receive credit for only one of 601.475/675. |
MWF 12-1:15p |
|
601.482 (E) |
MACHINE LEARNING: DEEP LEARNING (4) Unberath
Deep learning (DL) has emerged as a powerful tool for solving
data-intensive learning problems such as supervised learning for
classification or regression, dimensionality reduction, and control. As
such, it has a broad range of applications including speech and text
understanding, computer vision, medical imaging, and perception-based
robotics. Pre-req: Data Structures, Linear Algebra, Probability, Calc II required; Statistics, Machine Learning, Calc III, numerical optimization and Python strongly recommended. Students can receive credit for EN.601.482 or EN.601.682, but not both. |
MW 4:30-5:45p, F 4:30-5:20p |
|
601.485 (Q) |
PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille [Co-listed as AS.050.375/AS.050.675/EN.601.685.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks.
Pre-requisites: Calc I, programming experience (Python
preferred). Students can receive credit for at most one of
EN.601.485/EN.601.685/AS.050.375/AS.050.675.
|
TuTh 9-10:15 |
|
601.486 (E) |
MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT (3) Dredze
Advances in Artificial intelligence have opened new opportunities for
developing systems to aid in numerous areas of society. In order for
AI systems to succeed in making constructive and positive changes, we
must consider their impact on everyday life. Specifically, AI system
designers must evaluate the overall capabilities of the system,
consider the resulting human-AI interactions, and ensure that the
system behaves in a responsible and ethical manner. In this
project-based course you will work in teams of 3-5 students to 1)
Identify a need with high-impact implications on everyday life; 2)
Articulate principles of Responsible AI relevant to the intended
application, 3) Conceptualize and design an AI system targeting this
need, and 4) Develop the AI system by refining a demo-able prototype
based on feedback received during course presentations. Additionally,
we will discuss potential ethical issues that can arise in AI and how
to develop Responsible AI principles. Coursework will consist of
writing assignments, project presentations, and a project
demonstration.
Pre-req: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design). |
MW 1:30-2:45 |
|
601.489 (E) |
HUMAN-IN-THE-LOOP MACHINE LEARNING (3) Nalisnick
Machine learning (ML) is being deployed in increasingly consequential
tasks, such as healthcare and autonomous driving. For the foreseeable
future, successfully deploying ML in such settings will require close
collaboration and integration with humans, whether they be users,
designers, engineers, policy-makers, etc. This course will look at how
humans can be incorporated into the foundations of ML in a principled
way. The course will be broken down into three parts: demonstration,
collaboration, and oversight. Demonstration is about how machines can
learn from 'observing' humans---such as learning to drive a car from
data collected while humans drive. In this setting, the human is
assumed to be strictly better than the machine and so the primary goal
is to transmit the human's knowledge and abilities into the ML
model. The second part, collaboration, is about when humans and models
are near equals in performance but not in abilities. A relevant
setting is AI-assisted healthcare: perhaps a human radiologist and ML
model are good at diagnosing different kinds of diseases. Thus we will
look at methodologies that allow machines to ‘ask for help' when they
are either unconfident in their own performance and/or think the human
can better carry out the task. The course will close with the setting
in which machines are strictly better at a task than humans are, but
we still wish to monitor them to ensure safety and alignment with our
goals (oversight). Assessment will be done with homework, quizzes,
and a final project. Pre-req: EN.601.475/675. |
MW 1:30-2:45 |
|
601.490 (E) |
INTRO TO HUMAN-COMPUTER INTERACTION (3) Xiao & Reiter
This course is designed to introduce undergraduate and graduate
students to design techniques and practices in human-computer
interaction (HCI), the study of interactions between humans and
computing systems. Students will learn design techniques and
evaluation methods, as well as current practices and exploratory
approaches, in HCI through lectures, readings, and
assignments. Students will practice various design techniques and
evaluation methods through hands-on projects focusing on different
computing technologies and application domains. This course is
intended for undergraduate and graduate students in Computer
Science/Cognitive Science/Psychology. Interested students from
different disciplines should contact the instructor before enrolling
in this course. Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01 (Xiao): TuTh 3-4:15, limit 20, CS majors/minors |
|
601.501 |
COMPUTER SCIENCE WORKSHOP An applications-oriented, computer science project done under the supervision and with the sponsorship of a faculty member in the Department of Computer Science. Computer Science Workshop provides a student with an opportunity to apply theory and concepts of computer science to a significant project of mutual interest to the student and a Computer Science faculty member. Permission to enroll in CSW is granted by the faculty sponsor after his/her approval of a project proposal from the student. Interested students are advised to consult with Computer Science faculty members before preparing a Computer Science Workshop project proposal. Perm. of faculty supervisor req'd. |
See below for faculty section numbers |
|
601.503 |
INDEPENDENT STUDY Individual, guided study for undergraduate students under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See below for faculty section numbers |
|
601.507 |
UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See below for faculty section numbers and whether to select 507 or 517. |
|
601.509 |
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study and credit assigned must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit. S/U only. Permission required. |
See below for faculty section numbers |
|
601.513 |
GROUP UNDERGRADUATE PROJECT Independent learning and application for undergraduates under the direction of a faculty member in the department. This course has a regular project group meeting that students are expected to attend. The individual project contributions, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
Only for faculty specifically marked below. |
|
601.517 |
GROUP UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
Only for faculty specifically marked below. |
|
601.519 |
SENIOR HONORS THESIS (3) For computer science majors only. The student will undertake a substantial independent research project under the supervision of a faculty member, potentially leading to the notation "Departmental Honors with Thesis" on the final transcript. Students are expected to enroll in both semesters of this course during their senior year. Project proposals must be submitted and accepted in the preceding spring semester (junior year) before registration. Students will present their work publically before April 1st of senior year. They will also submit a first draft of their project report (thesis document) at that time. Faculty will meet to decide if the thesis will be accepted for honors. Prereq: 3.5 GPA in Computer Science after spring of junior year and permission of faculty supervisor. |
See below for faculty section numbers |
|
601.556 |
SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3) The student will undertake a substantial independent research project in the area of computer-integrated surgery, under joint supervision of a WSE faculty adviser and a clinician or clinical researcher at the Johns Hopkins Medical School. Prereq: 601.455 or perm req'd. |
Section 1: Taylor |
|
601.614 |
COMPUTER NETWORKS (3) Zhao Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-on programming assignments, homeworks and two exams. Required Course Background: C/C++ programming and data structures, or permission. Students can only receive credit for one of 601.414/614. |
TuTh 1:30 or 3-4:15p |
|
601.615 |
DATABASES Yarowsky Same material as 601.415, for graduate students. (www.cs.jhu.edu/~yarowsky/cs415.html) Required course background: Data Structures. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15 |
|
601.620 |
PARALLEL COMPUTING FOR DATA SCIENCE (3) Burns This course studies parallelism in data science, drawing examples from data analytics, statistical programming, and machine learning. It focuses mostly on the Python programming ecosystem but will use C/C++ to accelerate Python and Java to explore shared-memory threading. It explores parallelism at all levels, including instruction level parallelism (pipelining and vectorization), shared-memory multicore, and distributed computing. Concepts from computer architecture and operating systems will be developed in support of parallelism, including Moore’s law, the memory hierarchy, caching, processes/threads, and concurrency control. The course will cover modern data-parallel programming frameworks, including Dask, Spark, Hadoop!, and Ray. The course will not cover GPU deep-learning frameworks nor CUDA. The course is suitable for second-year undergraduate CS majors and graduate students from other science and engineering disciplines that have prior programming experience.
Required course background: 601.226 and 601.229 or equiv, familiarity with Python. |
MW 4:30-5:45pm |
|
601.621 |
OBJECT ORIENTED SOFTWARE ENGINEERING Darvish Same material as EN.601.421, for graduate students. This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. Required course background: intermediate programming, data structures, and experience in mobile or web app development. Students may receive credit for only one of 601.421/621. |
MWF 3-3:50p |
|
601.628 |
COMPILERS & INTERPRETERS Hovemeyer Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. Expected course background: 601.220, 601.226 & 601.229 required; 601.230 or 601.231 recommended |
MW 12-1:15 |
|
601.629 |
FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith
How can we effectively use functional programming techniques to build
real-world software? This course will primarily focus on using the
OCaml programming language for this purpose. Topics covered include
OCaml basics, modules, standard libraries, testing, quickcheck, build
tools, functional data structures and efficiency analysis, monads,
streams, and promises. Students will practice what they learn in
lecture via functional programming assignments and a final
project. |
MW 1:30-2:45pm |
|
601.631 |
THEORY OF COMPUTATION (3) Li This course covers the theoretical foundations of computer science. Topics included will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 601.231 and 601.431/601.631, unless one is for an undergrad degree and the other for grad. Required Background: discrete math or permission; discrete probability theory recommended. Students can receive credit for only one of EN.601.431/EN.601.631. |
TuTh 1:30-2:45 |
|
601.633 |
INTRO ALGORITHMS Dinitz Same material as 601.433, for graduate students. Required Background: Data Structures and (Discrete Math or Automata/Computation Theory). Students may receive credit for only one of 601.433/633. |
TuTh 9-10:15a |
|
601.641 |
BLOCKCHAINS AND CRYPTOCURRENCIES Green [Cross-listed in JHUISI.] Same as EN.601.441, for graduate students. Students may receive credit for only one of 600.451, 601.441, 601.641. Required course background: 601.226 and probability (any course). |
MW 12-1:15 |
|
601.643 |
SECURITY AND PRIVACY IN COMPUTING Rushanan & Martin Same material as 601.443, for graduate students. Required course background: C programming and computer system fundamentals. |
TuTh 12-1:15p |
|
601.647 |
COMPUTATIONAL GENOMICS: SEQUENCES Langmead Same material as 601.447, for graduate students. Required Course Background: Intermediate Programming (C/C++) and Data Structures. Students may earn credit for at most one of 601.447/647/747. |
TuTh 9-10:15 |
|
601.649 |
COMPUTATIONAL GENOMICS: APPLIED COMPARATIVE GENOMICS (3) Schatz [Formerly EN.601.749.] The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. Prereq: Prereq: working knowledge of the Unix operating system and programming expertise in R or Python. Students may receive credit for only one of EN.601.449, EN.601.649, EN.601.749. |
MW 3-4:15p |
|
601.654 |
INTRODUCTION TO AUGMENTED REALITY (3) Martin-Gomez
This course introduces students to the field of Augmented Reality. It
reviews its basic definitions, principles, and applications. The
course explains how fundamentals concepts of computer vision are
applied for the development of Augmented Reality applications. It then
focuses on describing the principal components and particular
requirements to implement a solution using this technology. The course
also discusses the main issues of calibration, tracking, multi-modal
registration, advanced visualization, and display
technologies. Homework in this course will relate to the mathematical
methods used for calibration, tracking, and visualization in augmented
reality.
Required course background: intermediate programming (C/C++), data structures, linear algebra. Students may receive credit for only one of 601.454 or 601.654, but not both. |
TuTh 3-4:15p |
|
601.655 |
COMPUTER INTEGRATED SURGERY I Taylor Same material as 601.455, for graduate students. (http://www.cisst.org/~cista/445/index.html) Prereq: data structures and linear algebra, or permission. Recommended: intermediate programming in C/C++, computer graphics, computer vision, image processing. Students may earn credit for 601.455 or 601.655, but not both. |
TuTh 1:30-2:45 |
|
601.661 |
COMPUTER VISION Katyal Same material as 601.461, for graduate students. Students may receive credit for at most one of 601.461/661/761. (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Required course background: intro programming & linear algebra & prob/stat |
Tu 4:30-7p |
|
601.663 |
ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard Same material as EN.601.463, for graduate students. Required course background: data structures, Calc III, linear algebra & prob/stat. Students may receive credit for only one of 601.463/663/763. |
Sec 01: TuTh 3-4:15p, limit 30, WSE + Non-ASEN grads |
|
601.664 |
ARTIFICIAL INTELLIGENCE Haque & Cachola Same as 601.464, for graduate students. Prereq: Data Structures; Recommended: linear algebra & prob/stat. Students can only receive credit for one of 601.464/664. |
Sec 01 [Haque]: TuTh 3-4:15p, limit 30, CS + MSEM grads |
|
601.665 |
NATURAL LANGUAGE PROCESSING Eisner Same material as 601.465, for graduate students. (www.cs.jhu.edu/~jason/465) Prerequisite: data structures and basic familiarity with Python, partial derivatives, matrix multiplication, and probabilities. Students may receive credit for at most one of 601.465/665. |
Lect: MWF 3-4:15 |
|
601.667 (E) |
INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. |
TuTh 9-10:15 |
|
601.668 |
MACHINE TRANSLATION Koehn Same material as 601.468, for graduate students. Required course background: prob/stat, data structures. Student may receive credit for at most one of 601.468/668. |
TuTh 1:30-2:45 |
|
601.670 |
ARTIFICIAL AGENTS (3) VanDurme This course covers a number of topics explored in introductory AI, such as knowledge representation, reasoning, and natural language understanding. Unlike introductory AI, we will pursue these topics based on the transformer neural architecture. We will motivate the material through interacting with assistive agents: how to build models that understand commands, how to generate responses back to a user, and how to reason about a synthetic environment to determine a course of action. Assignments will include programming, student presentations on readings, written summaries and quizzes on readings, and a final project. Required Course Background: (Machine Learning, or Machine Learning: Deep Learning, or Machine Translation, or Artificial System Design and Development), or (experience with pytorch or related environment and instructor approval). [601.475/675 OR 601.482/682 OR 601.468/668 OR 601.486/686] Students may receive credit for at most one of 601.470/670. |
MW 8:30-9:45a |
|
601.673 (E) |
COGNITIVE ARTIFICIAL INTELLIGENCE (3) Shu Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment.
Required Course Background: Prob/Stat & Linear Algebra &
Computing; prior course in ML/AI strongly recommended.
|
TuTh 1:30-2:45p |
|
EN.601.674 |
ML: LEARNING THEORY Arora [Formerly: Statistical Machine Learning] This is a graduate level course in machine learning. It will provide a formal and in-depth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine- learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. Required course background: multivariable calculus, probability, linear algebra, intro computing. Recommended: prior coursework in ML. Students may receive credit for only one of 601.474/674. |
MWF 12-1:15p |
|
601.675 |
MACHINE LEARNING Shpitser
Same material as 601.475, for graduate students.
Required course background: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Student may receive credit for only one of 601.475/675. |
MWF 12-1:15p |
|
601.682 |
MACHINE LEARNING: DEEP LEARNING Unberath
Same as 601.482, for graduate students.
Required course background: Data Structures, Linear Algebra, Probability, Calc II required; Statistics, Machine Learning, Calc III, numerical optimization and Python strongly recommended. Students may receive credit for 601.482 or 601.682 but not both. |
MW 4:30-5:45p, F 4:30-5:20p |
|
EN.601.685 |
PROBABILISTIC MODELS OF THE VISUAL CORTEX Yuille [Co-listed as AS.050.375/AS.050.675/EN.601.485.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. Pre-requisites: Calc I, programming experience (Python
preferred). Students can receive credit for at most one of
EN.601.485/EN.601.685/AS.050.375/AS.050.675.
|
TuTh 9-10:15 |
|
601.686 |
MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT Dredze
Advances in Artificial intelligence have opened new opportunities for
developing systems to aid in numerous areas of society. In order for
AI systems to succeed in making constructive and positive changes, we
must consider their impact on everyday life. Specifically, AI system
designers must evaluate the overall capabilities of the system,
consider the resulting human-AI interactions, and ensure that the
system behaves in a responsible and ethical manner. In this
project-based course you will work in teams of 3-5 students to 1)
Identify a need with high-impact implications on everyday life; 2)
Articulate principles of Responsible AI relevant to the intended
application, 3) Conceptualize and design an AI system targeting this
need, and 4) Develop the AI system by refining a demo-able prototype
based on feedback received during course presentations. Additionally,
we will discuss potential ethical issues that can arise in AI and how
to develop Responsible AI principles. Coursework will consist of
writing assignments, project presentations, and a project
demonstration.
Required course background: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design). |
MW 1:30-2:45 |
|
601.689 |
HUMAN-IN-THE-LOOP MACHINE LEARNING (3) Nalisnick
Machine learning (ML) is being deployed in increasingly consequential
tasks, such as healthcare and autonomous driving. For the foreseeable
future, successfully deploying ML in such settings will require close
collaboration and integration with humans, whether they be users,
designers, engineers, policy-makers, etc. This course will look at how
humans can be incorporated into the foundations of ML in a principled
way. The course will be broken down into three parts: demonstration,
collaboration, and oversight. Demonstration is about how machines can
learn from 'observing' humans---such as learning to drive a car from
data collected while humans drive. In this setting, the human is
assumed to be strictly better than the machine and so the primary goal
is to transmit the human's knowledge and abilities into the ML
model. The second part, collaboration, is about when humans and models
are near equals in performance but not in abilities. A relevant
setting is AI-assisted healthcare: perhaps a human radiologist and ML
model are good at diagnosing different kinds of diseases. Thus we will
look at methodologies that allow machines to ‘ask for help' when they
are either unconfident in their own performance and/or think the human
can better carry out the task. The course will close with the setting
in which machines are strictly better at a task than humans are, but
we still wish to monitor them to ensure safety and alignment with our
goals (oversight). Assessment will be done with homework, quizzes,
and a final project. Prerequisite: EN.601.475/675 or equivalent. |
MW 1:30-2:45 |
|
601.690 |
INTRO TO HUMAN-COMPUTER INTERACTION Xiao & Reiter
Same material as EN.601.490, for graduate students. Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01: TuTh 3-4:15, limit 15, CS only |
|
601.713 |
FUTURE NETWORKS Sabnani
This will be a graduate-level networking course. New applications
such as ones for metaverse require networking and computing to be
imbedded together. This feature is already beginning to be
implemented in 5G and 6G networks; 6G will also allow networks to
be used as sensors. These advances are enabled by new technologies
such as mobile edge computing, software-defined networking (SDN),
network slicing, digital twins, and named-data networking
(NDN). This course will start with introductory lectures on these
topics. Students will be asked to study new papers and do course
projects. These activities should result in longer term research
projects. |
Tu 4:30-7p |
|
601.723 CANCELLED |
ADVANCED TOPICS IN PARALLEL COMPUTING FOR DATA SCIENCE Burns This course will study recent advances in the systems support for scalable machine learning and artificial intelligence workloads. We will look at recent advances in programming languages, compilers, and operating systems and how they are being implemented in hardware accelerators. Topics will include data sparsity, single-instruction multi-threading, vectorization of memory and processing, and scatter/gather/reduce patterns. The course will alternate between lectures and seminar-style paper discussions. Each student will be expected to conduct and present a substantial research project individually or in teams. Required Course Background: 601.420/620 or equivalent. |
|
|
601.770 |
AI ETHICS AND SOCIAL IMPACT Field
AI is poised to have an enormous impact on society. What should that
impact be and who should get to decide it? The goal of this course
is to critically examine AI research and deployment pipelines,
with in-depth examinations of how we need to understand social
structures to understand impact. In application domains, we will
examine questions like “who are key stakeholders?”, “who is
affected by this technology?” and “who benefits from this
technology?”. We will also conversely examine: how can AI help us
learn about these domains, and can we build from this knowledge to
design AI for "social good"? As a graduate-level course, topics
will focus on current research including development and
deployment of technologies like large language models and decision
support tools, and students will conduct a final research project.
|
TuTh 12-1:15p |
|
601.771 |
ADVANCES IN SELF-SUPERVISED STATISTICAL MODELS Khashabi
The rise of massive self-supervised (pre-trained) models has
transformed various data-driven fields such as natural language
processing, computer vision, robotics, and medical imaging. This
advanced graduate course aims to provide a holistic view of the issues
related to these models: We will start with the history of how we got
here, and then delve into the latest success stories. We will then
focus on the implications of these technologies: social harms,
security risks, legal issues, and environmental impacts. The class
ends with reflections on the future implications of this trajectory.
|
TuTh 9-10:15a |
|
601.787 |
ADVANCED MACHINE LEARNING: MACHINE LEARNING FOR TRUSTWORTHY AI Liu This course teaches advanced machine learning methods for the design, implementation, and deployment of trustworthy AI systems. The topics we will cover include but are not limit to different types of robust learning methods, fair learning methods, safe learning methods, and research frontiers in transparency, interpretability, privacy, sustainability, AI safety and ethics. Students will learn the state-of-the-art methods in lectures, understand the recent advances by critiquing research articles, and apply/innovate new machine learning methods in an application. There will be homework assignments and a course project. Expected course background: 601.475/675 Machine Learning; recommended 601.476/676 ML: Data to Models and 601.482/682 Deep Learning. |
MW 3-4:15 |
|
601.788 |
MACHINE LEARNING FOR HEALTHCARE Oberst This course surveys the technical and practical challenges of applying machine learning in healthcare, focusing on two themes: The first theme will cover applications of machine learning to a wide range of healthcare data modalities (e.g., medical imaging, structured health records, etc). Beyond reviewing specific modeling approaches, we will focus on navigating pitfalls in model development and evaluation that arise in a healthcare context. The second theme will cover methodological approaches to developing safe and effective machine learning systems in healthcare, including topics such as (but not limited to) causality, fairness, and distribution shift. This course is designed for students who have a solid existing background in machine learning, and who are interested in both the technical and practical nuances of applying machine learning in healthcare. Grading will be done on the basis of homework assignments as well as a final project. Required course background: 601.475/675 Machine Learning. |
TuTh 9-10:15a |
|
601.801 |
COMPUTER SCIENCE SEMINAR Attendance recommended for all grad students; only 1st & 2nd year PhD students may register. |
TuTh 10:30-12 |
|
601.803 |
MASTERS RESEARCH Independent research for masters students. Permission required. |
See below for faculty section
numbers. |
|
601.805 |
GRADUATE INDEPENDENT STUDY Permission required. |
See below for faculty
section numbers. |
|
601.807 |
TEACHING PRACTICUM Selinski, Smith PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement. Permission required. |
limit 30 |
|
601.809 |
PHD RESEARCH Independent research for PhD students. |
See below for faculty section
numbers. |
|
601.811 |
FUTURE FACULTY: Preparing a New Generation of PIs for the Academic Job Search Unberath The goal of this seminar-style course is to prepare senior PhD students and postdocs in CS and robotics adjacent disciplines for the academic job market. At the end of the course sequence, it is expected that participants will 1) understand benefits and possible challenges of the academic career path, 2) be familiar with many aspects of the academic job market (such as timing, required documents, interview schedule, …), 3) have completed a first draft of their application documents to be further refined with their respective advisors and mentors, 4) be prepared to tackle phone and on-campus interviews, and 5) have an appreciation for the essential tasks junior faculty must master quickly. |
Mon 3-4:15p |
|
AS.050.814 |
RESEARCH SEMINAR IN COMPUTER VISION Yuille This course covers advanced topics in computational vision. It discusses and reviews recent progress and technical advances in visual topics such as object recognition, scene understanding, and image parsing. |
tba |
|
601.826 |
SELECTED TOPICS IN PROGRAMMING LANGUAGES Smith This course covers recent developments in the foundations of programming language design and implementation. topics covered vary from year to year. Students will present papers orally. |
Fr |
|
601.831 |
CS THEORY SEMINAR Dinitz, Li Seminar series in theoretical computer science. Topics include algorithms, complexity theory, and related areas of TCS. Speakers will be a mix of internal and external researchers, mostly presenting recently published research papers. |
W 12 |
|
601.864 |
SELECTED TOPICS IN MULTILINGUAL NATURAL LANGUAGE PROCESSING Yarowsky/Murray This is a weekly reading group focused on Natural Language Processing (NLP) outside of English. Whereas methods have gotten very strong in recent years on English NLP tasks, many methods fail on other languages due to both linguistic differences as well as lack of available annotated resources. This course will focus on Cross-Language Information Retrieval, Cross-Lingual Information Extraction, Multilingual Semantics, Massively Multilingual Language Modeling, and other non-English NLP sub-fields. Students will be expected to read, discuss, and present papers. Required course background: EN.601.465/665. |
Th 3p |
|
601.865 |
SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Eisner
A reading group exploring important current research in the field
and potentially relevant material from related fields. In addition to
reading and discussing each week's paper, enrolled
students are expected to take turns selecting papers and leading the discussion. |
W 12-1:15p |
|
601.866 |
SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme A seminar focussed on current research and survey articles on computational semantics. |
Fr 10:45-11:45 |
|
601.868 |
SELECTED TOPICS IN MACHINE TRANSLATION Koehn Students in this course will review, present, and discuss current research in machine translation. Prereq: permission of instructor. |
M 11-noon |
|
520.807 |
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING staff CLSP seminar series, for any students interested in current topics in language and speech processing. |
M & F 12-1:15 |
|
500.745 |
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Whitcomb, Vidal, Etienne-Cummings Seminar series in robotics. Topics include: Medical robotics, including computer-integrated surgical systems and image-guided intervention. Sensor based robotics, including computer vision and biomedical image analysis. Algorithmic robotics, robot control and machine learning. Autonomous robotics for monitoring, exploration and manipulation with applications in home, environmental (land, sea, space), and defense areas. Biorobotics and neuromechanics, including devices, algorithms and approaches to robotics inspired by principles in biomechanics and neuroscience. Human-machine systems, including haptic and visual feedback, human perception, cognition and decision making, and human-machine collaborative systems. Cross-listed with Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering. |
Wed 12-1:30 |
|
650.601 |
INTRODUCTION TO INFORMATION SECURITY Xiangyang Li This course exposes students to the cross-disciplinary and broad information security field. It surveys a range of fundamental topics of information security principles, architecture, policy and standard, risk management, cryptography, physical, operation, system and network security mechanisms, and law and ethics, among others. This course includes lectures, case studies, and homework. Students will also complete independent study class projects. Recommended Course Background: Basic knowledge of computer system and information technology. |
TuTh 12-1:15 |
|
650.614 |
RIGHTS IN THE DIGITAL AGE Michael Jacobs This course will examine various legal and policy issues presented by the tremendous growth in computer technology, especially the Internet. The rights that various parties have with respect to creating, modifying, using, distributing, storing, and copying digital data will be explored. The concurrent responsibilities, and potential liabilities, of those parties will also be addressed. The course will focus on intellectual property issues, especially copyright law, and other legal and economic considerations related to the use and management of digital data. Copyright law and its role within the framework of intellectual property law will be presented in a historical context with an emphasis on its applicability to emerging-technology issues. Specifically, the treatment of various works, such as music, film, and photography that were traditionally, analog in nature will be analyzed with respect to their treatment in the digital domain; works that are by their nature digital, such as computer software, will also be analyzed. The current state of U.S. copyright law will be presented, as will relevant international treaties and foreign laws. The goal of the course is to provide those involved or interested in digital rights management with a general awareness of the rights and obligations associated with maintaining and distributing digital data. (This course will be taught in Washington, DC and video-cast on Homewood Campus.) |
W 4:30-6:30p |
|
650.621 |
CRITICAL INFRASTRUCTURE PROTECTION Lanier Watkins This course focuses on understanding the history, the vulnerability, and the need to protect our Critical Infrastructure and Key Resources (CIKR). We will start by briefly surveying the policies which define the issues surrounding CIKR and the strategies that have been identified to protect them. Most importantly, we will take a comprehensive approach to evaluating the technical vulnerabilities of the 18 identified sectors, and we will discuss the tactics that are necessary to mitigate the risks associated with each sector. These vulnerabilities will be discussed from the perspective of ACM, IEEE or other technical journals/articles which detail recent and relevant network-level CIKR exploits. We will cover well known vulnerable systems such the Internet, SCADA or PLC and lesser known systems such as E911 and industrial robot. Also, a class project is required. Recommended Course Background: EN.650.424 or equivalent or permission by instructor. |
Th 4:30-7p |
|
650.656 |
COMPUTER FORENSICS Timothy Leschke This course introduces students to the field of computer forensics and it will focus on the various contemporary policy issues and applied technologies. Topics to be covered include: legal and regulatory issues, investigation techniques, data analysis approaches, and incident response procedures for Windows and UNIX systems. Homework in this course will relate to laboratory assignments and research exercises. Students should also expect that a group project will be integrated into this course. |
W 6:30-9:00p |
|
650.658 |
INTRODUCTION TO CRYPTOGRAPHY Xiangyang Li Cryptography has a rich history as one of the foundations of information security. This course serves as the introduction to the working primitives, development and various techniques in this field. It emphasizes reasoning about the constraint and construction of cryptographic protocols that use shared secret key or public key. Students will also be exposed to some current open problems. Permission of instructor only. |
MW 12-1:15p |
|
650.660 |
SOFTWARE VULNERABILITY ANALYSIS Reuben Johnston Competent execution of security assessments on modern software systems requires extensive knowledge in numerous technical domains and comprehensive understanding of security risks. This course provides necessary background knowledge and examines relevant theories for software vulnerabilities and exploits in detail. Key topics include historical vulnerabilities, their corresponding exploits, and associated risk mitigations. Fundamental tools and techniques for performing security assessments (e.g., software reverse engineering, static analysis, and dynamic analysis) are covered extensively. The format of this course includes lectures and assignments where students learn how to develop exploits to well-known historical vulnerabilities in a controlled environment. Students will complete and demonstrate a project as part of the course. |
TuTh 3-4:15p |
|
650.663 |
CLOUD COMPUTING SECURITY Reuben Johnston Cloud computing promises significant cost savings via economies of scale that typically are not achievable by a single organization. This course examines cloud computing in detail and introduces the security concerns associated with cloud computing. Key topics include service models for cloud computing, virtualization, storage, management, and data processing. Fundamental security principles are introduced and applied to cloud computing environments. The format of this course includes lectures and hands-on assignments. Students will complete a project and present it as part of the course. |
F 4:15-6:45p |
|
650.673 |
MOBILE AND WIRELESS SECURITY Ashutosh Dutta The past few decades have seen a rapid evolution of wireless LAN and cellular technologies. In addition to wireless access technologies, various types of network layer and application layer mobility protocols have been developed to provide seamless connectivity to mobile users. Maintaining end-to-end security for these mobile users needs to take into account authentication, authorization, integrity and confidentiality as mobile devices change their point-of-attachment. This course will provide an overview of various wireless access technologies, mobility protocol taxonomy and will describe end-to-end security including mobile end point, radio access network, network core, and application services. In addition, this will include hands-on lab experiments to examine security over wireless and mobile networks and a research group project. Overall objective of this course is to impart both theoretical and practical knowledge to the students, and at the same time make them ready for any future research to solve complex problems. Recommended Course Background: Knowledge of TCP/IP, Linux, Fundamentals of Networking. |
Fr 4:15-6:45 |
|
650.683 |
CYBERSECURITY RISK MANAGEMENT Javad Abed Data breaches, cyber attacks, cybercrime,
and information operations in social media continue to increase
in frequency and severity, causing businesses and governments to
focus more resources on cybersecurity risk management and
compliance. Utilizing real-world data breaches and attacks as
motivation, this course will provide students knowledge of risk
management concepts, frameworks, compliance regimes and best
industry practices used to ensure sound cybersecurity practices
in government, commercial, and academic organizations. Lab
exercises will provide opportunities for students to experience
key aspects of the risk management process and help prepare them
for post-graduation assignments as cybersecurity professionals.
|
W 1:30-4p |
|
650.836 |
INFORMATION SECURITY PROJECTS Dahbura, Li All MSSI programs must include a project involving a research and development oriented investigation focused on an approved topic addressing the field of information security and assurance from the perspective of relevant applications and/or theory. There must be project supervision and approval involving a JHUISI affiliated faculty member. A project can be conducted individually or within a team-structured environment comprised of MSSI students and an advisor. A successful project must result in an associated report suitable for on-line distribution. When appropriate, a project can also lead to the development of a so-called "deliverable" such as software or a prototype system. Projects can be sponsored by government/industry partners and affiliates of the Information Security Institute, and can also be related to faculty research programs supported by grants and Contracts. Required course for any full-time MSSI student. Open to MSSI students. Permission required for non-MSSI students. |
MW 11-11:50a |
|
650.840 |
INFORMATION SECURITY INDEPENDENT STUDY Xiangyang Li Individual study in an area of mutual interest to a graduate student and a faculty member in the Institute. |
|
01 - Xin Li 02 - Rao Kosaraju (emeritus) 03 - Soudeh Ghorbani 04 - Russ Taylor (ugrad research use 517, not 507) 05 - Scott Smith (use 513 for group project development) 06 - Joanne Selinski (use 513 for group project development) 07 - Michael Oberst 08 - Ali Madooei (use 513 for group project development) 09 - Greg Hager (ugrad research use 517, not 507) 10 - Craig Jones 11 - Sanjeev Khudhanpur [ECE] 12 - Yair Amir (emeritus) 13 - David Yarowsky 14 - Noah Cowan 15 - Randal Burns 16 - Jason Eisner (ugrad research use 517, not 507) 17 - Mark Dredze 18 - Michael Dinitz 19 - Rachel Karchin [BME] 20 - Michael Schatz 21 - Avi Rubin (emeritus) 22 - Matt Green 23 - Yinzhi Cao 24 - Raman Arora (ugrad research use 517, not 507) 25 - Najim Dehak [ECE] 26 - Misha Kazhdan 27 - David Hovemeyer (use 513 for group project development) 28 - Ali Darvish (use 513 for group project development) 29 - Alex Szalay [Physics] 30 - Peter Kazanzides 31 - Jerry Prince [BME] 32 - Carey Priebe [AMS] 33 - Anjalie Field 34 - Rene Vidal [BME] 35 - Alexis Battle (ugrad research use 517, not 507) [BME] 36 - Emad Boctor (ugrad research use 517, not 507) [SOM] 37 - Mathias Unberath 38 - Ben VanDurme 39 - Jeff Siewerdsen 40 - Vladimir Braverman 41 - Suchi Saria 42 - Ben Langmead 43 - Steven Salzberg 44 - Jean Fan [BME] 45 - Liliana Florea [SOM] 46 - Casey Overby Taylor [SPH] 47 - Philipp Koehn 48 - Abhishek Jain 49 - Anton Dahbura (ugrad research use 517, or 513 for group project development) 50 - Joshua Vogelstein [BME] 51 - Ilya Shpitser 52 = [Jessica Sorrell] 53 - Tamas Budavari [AMS] 54 - Alan Yuille 55 - Peng Ryan Huang 56 - Xin Jin 57 - Chien-Ming Huang 58 - Will Gray Roncal (ugrad research use 517, not 507) 59 - Kevin Duh [CLSP] 60 - Mihaela Pertea [BME] 61 - Archana Venkataraman [ECE] 62 - Matt Post [CLSP] 63 - Vishal Patel [ECE] 64 - Rama Chellappa [ECE] 65 - Mehran Armand [MechE] 66 - Jeremias Sulam [BME] 67 - Anqi Liu 68 - Yana Safanova 69 - Musad Haque 70 - Eric Nalisnik 71 - Amitabh Basu [AMS] 72 - Thomas Lippincott [CLSP] 73 - Joel Bader [BME] 74 - Daniel Khashabi 75 - Nicolas Loizou [AMS] 76 - Alejandro Martin Gomez 77 - Kenton Murray 78 - ?? (Azimi) 79 - Krishan Sabnani 80 - Nicholas Andrews [HTLCOE] 81 - Muyinatu (Bisi) Bell [ECE] 82 - Ziang Xiao 83 - Renjie Zhao 84 - Alex Marder 85 - Tianmin Shu 86 - Dawn Lawrie [HLTCOE]