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500.112 (E) |
GATEWAY COMPUTING: JAVA (3) staff 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. Sections start on the hour, from 9a - 4p. Sections 6 & 7 are restricted to incoming CS majors. |
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: Wed 4:30-6:30p, alternate weeks starting 9/4 |
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601.220 (E)
|
INTERMEDIATE PROGRAMMING (4) Hovemeyer/Darvish 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 601/600.107, 500.112, 500.113, 500.114, 580.200) or equivalent by permission. |
CS/CE majors/minors only |
|
601.226 (EQ) |
DATA STRUCTURES (4) Selinski/Madooei 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 600.107/601.107, 600.120/601.220, 500.112, 500.113+500.132, 500.114+500.132 or equivalent by permission. |
Sec 01: MWF 1:30-2:45, limit 75 |
|
601.229 (E) |
COMPUTER SYSTEM FUNDAMENTALS (3) Koehn/Hovemeyer We study the design and performance of a variety of computer systems from simple 8-bit micro-controllers through 32/64-bit RISC architectures all the way to ubiquitous x86 CISC architecture. We'll start from logic gates and digital circuits before delving into arithmetic and logic units, registers, caches, memory, stacks and procedure calls, pipelined execution, superscalar architectures, memory management units, etc. Along the way we'll study several typical instruction set architectures and review concepts such as interrupts, hardware and software exceptions, serial and other peripheral communications protocols, etc. A number of programming projects, frequently done in assembly language and using various processor simulators, round out the course. Prereq: 600.120/601.220. |
01: MWF 10, limit 90
|
|
601.231 (EQ) |
AUTOMATA and COMPUTATION THEORY (3) Kosaraju This course is an introduction to the theory of computing. topics include design of finite state automata, pushdown automata, linear bounded automata, Turing machines and phrase structure grammars; correspondence between automata and grammars; computable functions, decidable and undecidable problems, P and NP problems, NP-completeness, and randomization. Students may not receive credit for 601.231/600.271 and 601.631/600.471 for the same degree. Prereq: 550/553.171/172. |
01: TuTh 9-10:15, limit 75 |
|
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. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 600/601.226. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15 |
|
601.318 (E) |
OPERATING SYSTEMS (3) Huang This course covers the fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system. [Systems] Prereq: 600.120/601.220 & 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.318/418/618. |
TuTh 1:30-2:45 |
|
601.340 (E)
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WEB SECURITY (3) Cao This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems] Note: This undergrad version will not have the same paper component as the other versions of this course. Prerequisite: 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.340/440/640. |
TuTh 12-1:15 |
|
601.382 (E) |
DEEP LEARNING LAB (1) Unberath This course is an optional hands-on lab supplement for a few courses in the curriculum. It will provide tutorial support and practical experience for developing deep ML systems using PyTorch and TensorFlow, and may provide exposure to some other frameworks. It will also go into detail on practical methods for scalable learning on large data sets, and other more practical issues in setting up deep learning systems. Co-req: EN.601.482 or EN.601.682 or EN.601.765. |
Tu 4:30-6:30 |
|
601.415 (E) |
DATABASES (3) Yarowsky Similar material as 601.315, covered in more depth, for advanced undergraduates. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 600.226/601.226. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15 |
|
601.417 (E) |
The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive hands-on experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester (odd years) and is a good introduction course to the 601.717 Advanced Distributed Systems and Networks project-focused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses) Prereq: 600.120/601.220 & 600/601.226. Students may receive credit for only one of 601.317/417/617. |
TuTh 3-4:15 |
|
601.418 (E) |
OPERATING SYSTEMS (3) Huang Similar material as 600.318, covered in more depth, for advanced undergraduates. [Systems] Prereq: 600.120/601.220 & 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.318/418/618. |
TuTh 1:30-2:45 |
|
601.421 (E) |
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Facchinetti 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. [(Systems or Applications), Oral] (https://www.jhu-oose.com) Prereq: 600/601.226 & 600.120/601.220. Students may receive credit for only one of 601.421/621. |
MW 1:30-2:45 |
|
601.422 (E) |
SOFTWARE TESTING & DEBUGGING (3) Darvish Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. [Systems] Pre-req: EN.601.290 or EN.601.421. Students may receive credit for 601.422 or 601.622, but not both. |
TuTh 1:30-2:45 |
|
601.427 (E) |
PRINCIPLES OF PROGRAMMING LANGUAGES II (3) Smith This course is designed as a follow-on to Principles of Programming languages. It will cover a wide array of fundamental topics in programming languages, including advanced functional programming, the theory of inductive definitions, advanced operational semantics, advanced type systems, program analysis, program verification, theorem provers and SAT solvers. [Analysis] Pre-req: 601.426 or instructor permission. Students may receive credit for 601.427 or 601.627, but not both. |
MWF 10 |
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601.430 (EQ) |
COMBINATORICS AND GRAPH THEORY IN CS Li This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis] Pre-requisite: 550.171/553.171/553.172; probability theory and linear algebra recommended. Students may receive credit for only one of EN.601.430 and EN.601.630. |
TuTh 12-1:15 |
|
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. [Analysis] Prereq: 601/600.226 & (550/553.171/172 or 601.231/600.271) or Perm. Req'd. Students may receive credit for only one of 601.433/633. |
TuTh 12-1:15 |
|
601.434 (EQ) |
RANDOMIZED & BIG DATA ALGORITHMS (3) Braverman The course emphasizes algorithmic design aspects, and how randomization can be a helpful tool. The topics covered includee: tail inequalities, linear programming relaxation & randomized rounding, de-randomization, existence proofs, universal hashing, markov chains, metropolis and metropolis-hastings methods, mixing by coupling and by eigenvalues, counting problems, semi-definite programming and rounding, lower bound arguments, and applications of expanders. [Analysis] (www.cs.jhu.edu/~cs464) Prereq: 600.363/463/601.433/633 and (550.310/553.310/553.311 or 550.420/620 or equivalent). Students may receive credit for only one of 601.434/634. |
TuTh 12-1:15
|
|
601.440 (E)
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WEB SECURITY (3) Cao This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems] Prerequisite: 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.340/440/640. |
TuTh 12-1:15 |
|
601.442 (EQ) |
MODERN CRYPTOGRAPHY (3) Jain Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zero-knowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. [Analysis] Prerequisite: 601.231/600.271/471 & (550.310/553.310 or 553.331 or 550.420/553.420). Students may receive credit for only one of 601.442/642. |
MW 1:30-2:45 |
|
601.443 (E) |
SECURITY AND PRIVACY IN COMPUTING (3) Rubin 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. [Applications] Prerequisite: 600.233/601.229 & (600/601.318/418 or 600.444/601.414) Students may receive credit for only one of 601.443/643. |
TuTh 9-10:15 |
|
601.444 (E) |
NETWORK SECURITY (3) Nielson This course focuses on communication security in computer systems and networks. The course is intended to provide students with an introduction to the field of network security. The course covers network security services such as authentication and access control, integrity and confidentiality of data, firewalls and related technologies, Web security and privacy. Course work involves implementing various security techniques. A course project is required. [Systems] Prerequisites: 600.120/601.220, 600/601.226, 600.344/444/601.314/414/614 or permission. Students can receive credit for only one of 601.444/644. |
MW 3-4:15 |
|
601.447 (E) |
COMPUTATIONAL GENOMICS: SEQUENCES (3) Langmead/Solomon 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 nprojects. [Applications, Oral] Prereq: 600.120/601.220 & 600/601.226. Students may receive credit for at most one of 601.447/647/747. |
TuTh 12-1:15 |
|
601.452 (E) |
COMPUTATIONAL BIOMEDICAL RESEARCH (3) Schatz
[Co-listed with AS.020.415] This course for advanced undergraduates includes classroom instruction in interdisciplinary research approaches and lab work on an independent research project in the lab of a Bloomberg Distinguished Professor and other distinguished faculty. Lectures will focus on cross-cutting techniques such as data visualization, statistical inference, and scientific computing. In addition to two 50-minute classes per week, students will commit to working approximately 3 hours per week in the lab of one of the professors. The student and professor will work together to schedule the research project. Students will present their work at a symposium at the end of the semester. Prereq: permission required. |
MW 3-3:50 |
|
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. [Applications] (http://www.cisst.org/~cista/445/index.html) Prereq: 600/601.226 and linear algebra, or permission. Recmd: 600.120/601.220, 600/601.457, 600/601.461, image processing. Students may earn credit for only one of 601.455/655. |
TuTh 1:30-2:45 |
601.457 (EQ) |
COMPUTER GRAPHICS (3) Kazhdan This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. [Applications] Prereq: no audits; 600.120/601.220 & 600/601.226 & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.
|
MWF 11 |
|
601.461 (EQ) |
COMPUTER VISION (3) Hager This course gives an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3-D geometry from binocular stereo, motion, and photometric stereo; and object recognition. Edge detection and color perception are covered as well. Elements of machine vision and biological vision are also included. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Prereq: intro programming, linear algebra, prob/stat. Students can earn credit for at most one of 601.461/661/761. |
TuTh 9-10:15 |
|
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. [Analysis] Prereq: 600.226/601.226 & linear algebra & probability. Students may receive credit for only one of 600.336/436/636 and 601.463/663/763. |
TuTh 4:30-5:45 |
601.464 (E) |
ARTIFICIAL INTELLIGENCE (3) VanDurme 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. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 |
TuTh 9-10:15 |
|
601.465 (E) |
NATURAL LANGUAGE PROCESSING (3) Duh This course is an in-depth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers? The course covers methods for trees (parsing and semantic interpretation), sequences (finite-state transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech tagging, speech recognition and machine translation. There are a number of structured but challenging programming assignments. [Applications] (www.cs.jhu.edu/~jason/465) Prerequisite: 600/601.226. Students may receive credit for at most one of 601.465/665. |
MWF 11 |
|
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. [Applications] Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. |
MW 3-4:15 |
|
601.475 (E) |
MACHINE LEARNING (3) Dredze
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.
[Applications or Analysis] Pre-reqs: multivariable calculus (110.202 or 110.211) & probability (550.310/553.310/553.311 or 550.420/553.420 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.601.220/600.120, AS.250.205, EN.580.200, EN.600/601.107). Students may receive credit for only one of 601.475/675. |
MW 1:30-2:45 |
|
601.477 (EQ) |
CAUSAL INFERENCE (3) Shpitser "Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. [Analysis] Pre-requisites: familiarity with R programming and (600/601.475/675 or stats/probability) or permission. Students may receive credit for at most one of 601.477/677. |
TuTh 3-4:15 |
|
601.482 (E) |
MACHINE LEARNING: DEEP LEARNING (3) 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: (AS.110.201 or AS.110.212 or EN.553.291) and (EN.553.310 EN.553.311 or EN.553.420 or EN.560.348) and (EN.601.475 or equiv); Calc III and numerical optimization recommended. Recommended co-req: EN.601.382. |
TuTh 12-1:15 |
|
AS.050.375 (Q) |
PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille [Was EN.601.485, now cross-listed as AS.050.375] 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. [Applications or Analysis] Pre-requisites: Calc I, programming experience (Python preferred). |
TuTh 9-10:15 |
|
601.490 (E) |
INTRO TO HUMAN-COMPUTER INTERACTION (3) C-M Huang
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. [Applications] Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
TuTh 3-4:15 |
|
601.501 |
COMPUTER SCIENCE WORKSHOP [Formerly 600.491] 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 |
|
601.517 |
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: 600.445 or perm req'd. |
Section 1: Taylor |
|
601.615 |
DATABASES Yarowsky Same material as 601.415, for graduate students. [Systems] (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 |
|
601.617 |
The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive hands-on experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester (odd years) and is a good introduction course to the 601.717 Advanced Distributed Systems and Networks project-focused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses) Prereq: Intermediate Programming (C/C++) and Data Structures. Students may receive credit for only one of 601.317/417/617. |
TuTh 3-4:15 |
|
601.618 |
OPERATING SYSTEMS Huang Same material as 601.418, for graduate students. [Systems] Required course background: Data Structures & Computer System Fundamentals. Students may receive credit for only one of 601.318/418/618. |
TuTh 1:30-2:45 |
|
601.621 |
OBJECT ORIENTED SOFTWARE ENGINEERING Facchinetti Same material as 601.421, for graduate students. [Systems or Applications] (https://www.jhu-oose.com) Required course background: Intermediate Programming & Data Structures. Students may receive credit for only one of 601.421/621. |
MW 1:30-2:45 |
|
601.622 |
SOFTWARE TESTING & DEBUGGING (3) Darvish Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. [Systems] Pre-req: EN.601.290 or EN.601.421 or EN.601.621. Students may receive credit for 601.422 or 601.622, but not both. |
TuTh 1:30-2:45 |
|
601.627 |
PRINCIPLES OF PROGRAMMING LANGUAGES II (3) Smith This course is designed as a follow-on to Principles of Programming languages. It will cover a wide array of fundamental topics in programming languages, including advanced functional programming, the theory of inductive definitions, advanced operational semantics, advanced type systems, program analysis, program verification, theorem provers and SAT solvers. [Analysis] Pre-req: 601.426 or instructor permission. Students may receive credit for 601.427 or 601.627, but not both. |
MWF 10 |
|
601.630 |
COMBINATORICS AND GRAPH THEORY IN CS Li This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis] Required Background: discrete math; probability theory and linear algebra recommended. Student may receive credit for only one of 601.430/601.630. |
TuTh 12-1:15 |
|
601.633 |
INTRO ALGORITHMS Dinitz Same material as 600.433, for graduate students. [Analysis] Required Background: Data Structures and (Discrete Math or Automata/Computation Theory). Students may receive credit for only one of 601.433/633. |
TuTh 12-1:15 |
|
601.634 |
RANDOMIZED & BIG DATA ALGORITHMS Braverman Same material as 601.434, for graduate students. [Analysis] (www.cs.jhu.edu/~cs464) Required Background: Algorithms and probability. Students may receive credit for only one of 601.434/634. |
TuTh 12-1:15
|
|
601.640 |
WEB SECURITY (3) Cao This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems] Required course background: data structures and computer system fundamentals. Students may receive credit for only one of 601.340/440/640. |
TuTh 12-1:15 |
|
601.642 |
MODERN CRYPTOGRAPHYJain Same material as 601.442, for graduate students. [Analysis] Required course background: Probability & Automata/Computation Theory. |
MW 1:30-2:45 |
|
601.643 |
SECURITY AND PRIVACY IN COMPUTING Rubin Same material as 601.443, for graduate students. [Applications] Required course background: A basic course in operating systems and networking, or permission of instructor. |
TuTh 9-10:15 |
|
601.644 |
NETWORK SECURITY Nielson [Cross-listed in ISI] Same material as 601.444, for graduate students. [Systems] Required course background: 600.120, 600.226, Computer Networks or permission. Students may receive credit for only one of 601.444/644. |
MW 3-4:15 |
|
601.647 |
COMPUTATIONAL GENOMICS: SEQUENCES Langmead/Solomon Same material as 601.447, for graduate students. [Applications] Required Course Background: Intermediate Programming (C/C++) and Data Structures. Students may earn credit for at most one of 601.447/647/747. |
TuTh 12-1:15 |
|
601.655 |
COMPUTER INTEGRATED SURGERY I Taylor Same material as 601.455, for graduate students. [Applications] (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 |
601.657 |
COMPUTER GRAPHICS Kazhdan Same material as 601.457, for graduate students. Prereq: no audits; Intermediate Programming (C/C++) & Data Structures & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.
|
MWF 11 |
|
601.661 |
COMPUTER VISION Shen Same material as 601.461, for graduate students. Students may receive credit for at most one of 601.461/661/761. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Required course background: intro programming & linear algebra & prob/stat |
TuTh 4:30-5:45 (was 9-10:15) |
|
601.663 |
ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard Same material as EN.601.463, for graduate students. [Analysis] Required course background: data structures & linear algebra & prob/stat. Students may receive credit for only one of 600.336/436/636 or 601.463/663/763. |
TuTh 4:30-5:45 |
|
601.664 (600.435) |
ARTIFICIAL INTELLIGENCE VanDurme Same as 601.464, for graduate students. [Applications] Prereq: Data Structures; Recommended: linear algebra & prob/stat. Students can only receive credit for one of 601.464/664 |
TuTh 9-10:15 |
|
601.665 |
NATURAL LANGUAGE PROCESSING Duh Same material as 601.465, for graduate students. [Applications] (www.cs.jhu.edu/~jason/465) Prerequisite: data structures. Students may receive credit for at most one of 601.465/665. |
MWF 11 |
|
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. [Applications] Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. |
MW 3-4:15 |
|
601.675 |
MACHINE LEARNING Dredze
Same material as 601.475, for graduate students.
[Applications or Analysis] Required course background: multivariable calculus, probability, linear algebra, intro computing. Student may receive credit for only one of 601.475/675. |
MW 1:30-2:45 |
|
601.677 |
CAUSAL INFERENCE Shpitser Same material as 601.477, for graduate students. [Analysis] Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Students may receive credit for at most one of 601.477/677. |
TuTh 3-4:15 |
|
601.682 |
MACHINE LEARNING: DEEP LEARNING Unberath
Same as 601.482, for graduate students. [Applications]
Required course background: probability and linear algebra, some machine learning; calc III and numerical optimization recommended. Recommended co-req: EN.601.382. |
TuTh 12-1:15 |
|
AS.050.675 |
PROBABILISTIC MODELS OF THE VISUAL CORTEX Yuille [Was EN.601.685, now cross-listed as AS.050.675.] 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. [Applications or Analysis] Pre-requisites: Calc I, programming experience (Python preferred). |
TuTh 9-10:15 |
|
601.690 |
INTRO TO HUMAN-COMPUTER INTERACTION C-M Huang
Same material as EN.601.490, for graduate students. [Applications] Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
TuTh 3-4:15 |
|
601.714 |
ADVANCED COMPUTER NETWORKS Jin
This is a graduate-level course on computer networks. It provides
a comprehensive overview on advanced topics in network protocols
and networked systems. The course will cover both classic papers
on Internet protocols and recent research results. It will examine
a wide range of topics, e.g., routing, congestion control, network
architectures, datacenter networks, network virtualization,
software-defined networking, and programmable networks, with an
emphasize on core networking concepts and principles. The course
will include lectures, paper discussions, programming assignments
and a research project. [Systems] Pre-req: EN.601.414/614 or equivalent. |
TuTh 1:30-2:45 |
|
601.745 |
ADVANCED TOPICS IN APPLIED CRYPTOGRAPHY (3) Green [Cross-listed in ISI] This reading and project based course will explore the latest research in the area of applied cryptography and cryptographic engineering. Topics covered will include zero knowledge, efficient multiparty computation, cryptocurrencies, and trusted computing hardware. Readings will be drawn from the latest applied cryptography and security conferences. The course will include both reading, critical analysis, presentations and a course programming project. [Analysis or Applications] Prereq: 600/650.454 or 601.445/645 (Practical Crypto) or 600/601.442/642 or permission. |
MW 12-1:15 |
|
580.745 |
MATHEMATICS OF DEEP LEARNING Vidal [cross-listed from BME, 1.5 credits only] The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. For example, a key issue is that the training problem is nonconvex, hence optimization algorithms are not guaranteed to return a global minima. Another key issue is that while the size of deep networks is very large relative to the number of training examples, deep networks appear to generalize very well to unseen examples and new tasks. This course will overview recent work on the theory of deep learning that aims to understand the interplay between architecture design, regularization, generalization, and optimality properties of deep networks. |
Fr 1-3p |
|
601.778 ADDED! |
ADVANCED TOPICS IN CAUSAL INFERENCE (3) Shpitser This course will cover advanced topics on all areas of causal inference, including learning causal effects, path-specific effects, and optimal policies from data featuring biases induced by missing data, confounders, selection, and measurement error, techniques for generalizing findings to different populations, complex probabilistic models relevant for causal inference applications, learning causal structure from data, and inference under interference and network effects. The course will feature a final project which would involve either an applied data analysis problem (with a causal inference flavor), a literature review, or theoretical work. [Analysis] Pre-requisite: EN.600.477/677 or permission. |
TuTh 4:30-5:45 |
|
601.779 |
MACHINE LEARNING: ADVANCED TOPICS (3) Arora
[Formerly called Advanced topics in Representation Learning] Pre-requisites: Representation Learning or
permission (requiring all of the following):
|
MW 3-4:15 |
|
601.801 |
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 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 25 |
|
601.809 |
PHD RESEARCH Independent research for PhD students. |
See below for faculty section
numbers. |
|
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 |
|
601.814 |
SELECTED TOPICS IN COMPUTER NETWORKS Jin In this course we will read, discuss and present classic papers and current research in computer networks. The topic coverage will vary each semester. |
W 4-5 |
|
601.817 |
SELECTED TOPICS IN SYSTEMS RESEARCH R.Huang This course covers latest advances in the research of computer systems including operating systems, distributed system, mobile and cloud computing. Students will read and discuss recent research papers in top systems conferences. Each week, one student will present the paper and lead the discussion for the week. The focus topics covered in the papers vary semester to semester. Example topics include fault-tolerance, reliability, verification, energy efficiency, and virtualization. |
Fr 1-2:15 |
|
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. |
Th 1-2 |
|
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 |
|
601.845 |
SELECTED TOPICS IN APPLIED CRYPTOGRAPHY Green In this course students will read, discuss and present current research papers in applied cryptography. Topic coverage will vary each semester. Prereq: permission of instructor. |
Tu 12-12:50 |
|
601.857 | SELECTED TOPICS IN COMPUTER GRAPHICS Kazhdan In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week. |
W 1:30-2:45 |
|
601.865 |
SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Sedoc A reading group exploring important current research in the field and potentially relevant material from related fields. Enrolled students are expected to present papers and lead discussion. |
Th 12 |
|
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. |
W 11-noon |
601.875 |
SELECTED TOPICS IN MACHINE LEARNING Arora This seminar is recommended for all students interested in data intensive computing research areas (e.g., machine learning, computer vision, natural language processing, speech, computational social science). The meeting format is participatory. Papers that discuss best practices and the state-of-the-art across application areas of machine learning and data intensive computing will be read. Student volunteers lead individual meetings. Faculty and external speakers present from time-to-time. Required course background: a machine learning course or permission of instructor. |
CANCELED (was Thu 3-4:15) |
|
520.702 |
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING staff CLSP seminar series, for any students interested in current topics in language and speech processing. |
Tu & Fr 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 |
01 - Xin Li 02 - Rao Kosaraju 03 - Soudeh Ghorbani 04 - Russ Taylor (ugrad research use 517, not 507) 05 - Scott Smith 06 - Joanne Selinski 07 - Harold Lehmann 08 - Joao Sedoc [John Sheppard] 09 - Greg Hager 10 - Gregory Chirikjian 11 - Sanjeev Khudhanpur 12 - Yair Amir 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 20 - Michael Schatz 21 - Avi Rubin 22 - Matt Green 23 - Yinzhi Cao 24 - Raman Arora (ugrad research use 517, not 507) 25 - Rai Winslow 26 - Misha Kazhdan 27 - Chris Callison-Burch 28 - Ali Darvish 29 - Alex Szalay 30 - Peter Kazanzides 31 - Jerry Prince 32 - Carey Priebe 33 - Nassir Navab 34 - Rene Vidal 35 - Alexis Battle (ugrad research use 517, not 507) 36 - Emad Boctor (ugrad research use 517, not 507) 37 - Mathias Unberath 38 - Ben VanDurme 39 - Jeff Siewerdsen 40 - Vladimir Braverman 41 - Suchi Saria 42 - Ben Langmead 43 - Steven Salzberg 44 - Tal Linzen 45 - Liliana Florea 46 - Casey Overby Taylor 47 - Philipp Koehn 48 - Abhishek Jain 49 - Anton Dabhura (ugrad research use 517, not 507) 50 - Joshua Vogelstein 51 - Ilya Shpitser 52 - Austin Reiter 53 - Tamas Budavari 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 60 - Mihaela Pertea [Marin Kobilarov]