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.

All sections are limited to incoming freshmen for Fall 2018. Sections 5 (1p) and 6 (2p) are intended for CS majors. Sections start on the hour, from 9a - 4p.

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 9/5
Sec 02: Wed 4:30-6:30p, alternate weeks starting 9/12
limit 19 each, CS majors only

601.107 (E)

INTRO TO PROGRAMMING IN JAVA (3) More

This course introduces fundamental structured and object-oriented programming concepts and techniques, using Java, and is intended for all who plan to use computer programming in their studies and careers. Topics covered include variables, arithmetic operations, control structures, arrays, functions, recursion, dynamic memory allocation, text files, class usage and class writing. Program design and testing are also covered, in addition to more advanced object-oriented concepts including inheritance and exceptions as time permits. First-time programmers are strongly advised to take 601.108 concurrently in Fall/Spring semesters.

Prereq: familiarity with computers. Students may receive credit for 601.107 or 600.112, but not both.

MW 1:30-2:45, limit 90 (was 120)

601.108 (E)

INTRO PROGRAMMING LAB (1) More

Satisfactory/Unsatisfactory only. This course is intended for novice programmers and must be taken in conjunction with 600.107. The purpose of this course is to give novice programmers extra hands-on practice with guided supervision. Students will work in pairs each week to develop working programs, with checkpoints for each development phase. Students may receive credit for 601.108 or 600.113, but not both.

Co-req: 601.107.

Sec 1: Wed 6:00-9:00p, limit 18
Sec 2: Thu 4:30-7:30p, limit 18

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 601/600.107, 600.112, 580.200) or equivalent by permission.

CS/CE majors/minors only
Sec 01: MWF 12:00-1:15 (More)
Sec 02: MWF 1:30-2:45 (incoming CS freshman only) (Darvish)
Sec 03: MWF 3:00-4:15 (Selinski)
Sec 04: TuTh 9-10:15, Fr 8:30-9:45 (More)
limit 34/section

601.226 (E,Q)

DATA STRUCTURES (4) Schatz

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 or equivalent by permission.

Sec 01 (): MWF 1:30-2:45, limit 125

601.229 (E)

COMPUTER SYSTEM FUNDAMENTALS (3) Froehlich

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.

MW 4:30-5:45 (was MWF 10)
limit 75

601.231 (E,Q)

AUTOMATA and COMPUTATION THEORY (3) Li

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: 553/550.171/172.

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
limit 30

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 and 600.233/601.229. Students may receive credit for only one of 601.318/418/618.

TuTh 1:30-2:45
limit 20

601.340 (E)

NEW COURSE!

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
limit 5

601.382 (E)

DEEP LEARNING LAB (1) Hager

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
S/U only
limit 60

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. Students may receive credit for only one of 601.315/415/615.

TuTh 3-4:15
limit 20

601.418
600.418 (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 and 600.233/601.229. Students may receive credit for only one of 601.318/418/618.

TuTh 1:30-2:45
limit 15

601.421
600.321 (E)

OBJECT ORIENTED SOFTWARE ENGINEERING (3) Smith

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] (http://pl.cs.jhu.edu/oose/index.shtml)

Prereq: 600/601.226 and 600.120/601.220. Students may receive credit for only one of 601.421/621.

MW 1:30-2:45
limit 75

601.433
600.463 (E,Q)

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: 600.226 and 550.171/172 or Perm. Req'd. Students may receive credit for only one of 601.433/633.

TuTh 1:30-2:45
limit 60

601.440 (E)

NEW COURSE!

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
limit 5

601.442
600.442 (E,Q)

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: 600.271/471 and 550.310/550.420. Students may receive credit for only one of 601.442/642.

MW 1:30-2:45
limit 25

601.443
600.443 (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 and (600.318/418 or 600.444) Students may receive credit for only one of 601.443/643.

TuTh 9-10:15
limit 30

601.444
600.424 (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
limit 20

601.445
600.454 (E)

PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green

[Co-listed with 650.445.] This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem. We will place a particular emphasis on the techniques of provable security and the feasibility of reverse-engineering undocumented cryptographic systems. [Systems]

Prereq: EN.600/601.226 and EN.600.233/601.229. Students may receive credit for only one of 601.445/645.

MW 12-1:15
limit 25

601.455
600.445 (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
limit 40

601.457
600.457 (E,Q)

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 (C++), 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
limit 40

601.461
600.461 (E,Q)

COMPUTER VISION (3) Ali

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.

MW 4:30-5:45 (was TuTh 12-1:15)
limit 35

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, 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
limit 30

601.464 600.435 (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
limit 40

601.465
600.465 (E)

NATURAL LANGUAGE PROCESSING (4) Eisner

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 3-4:15, section T 6-7:30p
limit 30

601.468
600.468 (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. [Applications]

Required course background: prob/stat, 600/601.226. Student may receive credit for at most one of 601.468/668.

TuTh 1:30-2:45
limit 20

601.475
600.475 (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 (AS.110.202), probability (EN.550.310/EN.550.420), linear algebra (AS.110.201/AS.110.212). Students may receive credit for only one of 601.475/675.

MW 1:30
limit 50

601.477 (E,Q)

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: 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
limit 30

601.481 (E,Q)
NEW COURSE!

MACHINE LEARNING: OPTIMIZATION (3) Arora

Optimization is at the heart of machine learning. Most machine learning problems can be posed as optimization problems. However, unlike mathematical optimization where the focus is on efficient algorithms for finding solutions with a high degree of accuracy as measured by optimality conditions, optimization for machine learning focuses on algorithms that are efficient and generalize well. In this course, we will focus on optimization for problems that arise in machine learning, design and analysis of algorithms for solving these problems, and the interplay of optimization and machine learning. The coursework will include homework assignments and a final project focusing on applying optimization algorithms to real world machine learning problems. [Analysis or Applications]

Pre-requisites: EN.600.475 Machine Learning or all of the following:

1. Linear algebra (vector spaces, normed vectors spaces, inner product spaces, singular value decomposition)
2. Probability and Statistics (random variables, probability distributions, expectation, mean, variance, covariance, conditional probability, Bayes rule)
3. Introductory machine learning (classification, regression, empirical risk minimization, regularization)
4. Multivariate calculus (partial derivative, gradient, Jacobian, Hessian, critical points)

MWF 3-4:15
limit 30

601.482 (E)

MACHINE LEARNING: DEEP LEARNING (3) Hager

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.
The goal of this course is to introduce the basic concepts of deep learning (DL). The course will include a brief introduction to the basic theoretical and methodological underpinnings of machine learning, commonly used architectures for DL, DL optimization methods, DL programming systems, and specialized applications to computer vision, speech understanding, and robotics.
Students will be expected to solve several DL problems on standardized data sets, and will be given the opportunity to pursue team projects on topics of their choice. [Applications]
Students should also consider taking EN.601.382 Deep Learning Lab as a supplement. Students may choose to skip the lab course if they already have a strong programming background and are comfortable learning on their own using online resources and tutorials.

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); Calc III and numerical optimization recommended. Recommended co-req: EN.601.382.

TuTh 12-1:15
limit 30

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
limit 25

601.490 (E)

NEW COURSE!

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
Sec 01: CS majors/minors, limit 20
Sec 02: KSAS students, limit 10

601.501
600.591

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
600.503

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
600.507

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
600.509

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
600.517

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
600.519

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
600.546

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: 600.226. Students may receive credit for only one of 601.315/415/615.

TuTh 3-4:15
limit 43

601.618

OPERATING SYSTEMS Huang

Same material as 601.418, for graduate students. [Systems]

Required course background: 600.226 and 600.233. Students may receive credit for only one of 601.318/418/618.

TuTh 1:30-2:45
limit 20

601.621
600.421 (E)

OBJECT ORIENTED SOFTWARE ENGINEERING Smith

Same material as 601.421, for graduate students. [Systems or Applications] (http://pl.cs.jhu.edu/oose/index.shtml)

Required course background: 600.226 and 600.120. Students may receive credit for only one of 601.421/621.

MW 1:30-2:45
limit 28

601.631
600.471 (EQ)

CANCELED

THEORY OF COMPUTATION (3) Li

This is a graduate-level course studying the theoretical foundations of computer science. Topics covered 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 601.631, unless one is for an undergrad degree and the other for grad. [Analysis]

Prereq: discrete math or permission.

CANCELED
limit 40

601.633

INTRO ALGORITHMS Dinitz

Same material as 600.433, for graduate students. [Analysis]

Prereq: 600.226 and 550.171/172 or Perm. Req'd. Students may receive credit for only one of 601.433/633.

TuTh 1:30-2:45
limit 60

601.640

NEW COURSE!

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
limit 20

601.642

MODERN CRYPTOGRAPHYJain

Same material as 601.442, for graduate students. [Analysis]

Required course background: Probability and 600.271/471 or equiv.

MW 1:30-2:45
limit 25

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
limit 40

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
limit 35

601.645

PRACTICAL CRYPTOGRAPHIC SYSTEMS Green

[Co-listed with 650.445.] Same material as 601.445, for graduate students. [Systems]

Prereqs? Students may receive credit for only one of 601.445/645.

MW 12-1:15
limit 30

601.655
600.645

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
limit 30

601.657

COMPUTER GRAPHICS Kazhdan

Same material as 601.457, for graduate students.

Prereq: no audits; 600.120 (C++), 600.226, 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
limit 35

601.661

COMPUTER VISION Ali

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

MW 4:30-5:45 (was TuTh 12-1:15)
limit 30

601.663

ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard

Same material as EN.601.463, for graduate students. [Analysis]

Required course background: 600.226, calculus, prob/stat. Students may receive credit for only one of 600.336/436/636 or 601.463/663/763.

TuTh 4:30-5:45
limit 20

601.664 (600.435)

ARTIFICIAL INTELLIGENCE VanDurme

Same as 601.464, for graduate students. [Applications]

Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664

TuTh 9-10:15
limit 40

601.665

NATURAL LANGUAGE PROCESSING Eisner

Same material as 601.465, for graduate students. [Applications] (www.cs.jhu.edu/~jason/465)

Prerequisite: 600.226. Students may receive credit for at most one of 601.465/665.

MWF 3-4:15, section T 6-7:30p
limit 30

601.668

MACHINE TRANSLATION Koehn

Same material as 601.468, for graduate students. [Applications]

Required course background: prob/stat, 600.226. Student may receive credit for at most one of 601.468/668.

TuTh 1:30-2:45
limit 40

601.675

MACHINE LEARNING Dredze

Same material as 601.475, for graduate students. [Applications or Analysis]

Required course background: multivariable calculus, probability, linear algebra. Student may receive credit for only one of 601.475/675.

MW 1:30
limit 70

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
limit 25

601.681
NEW COURSE!

MACHINE LEARNING: OPTIMIZATION Arora

Same material as 601.481, for graduate students. [Analysis or Applications]

Pre-requisites: EN.600.475 Machine Learning or all of the following:

1. Linear algebra (vector spaces, normed vectors spaces, inner product spaces, singular value decomposition)
2. Probability and Statistics (random variables, probability distributions, expectation, mean, variance, covariance, conditional probability, Bayes rule)
3. Introductory machine learning (classification, regression, empirical risk minimization, regularization)
4. Multivariate calculus (partial derivative, gradient, Jacobian, Hessian, critical points)

MWF 3-4:15
limit 30

601.682

MACHINE LEARNING: DEEP LEARNING Hager

Same as 601.482, for graduate students. [Applications]
Students should also consider taking EN.601.382 Deep Learning Lab as a supplement. Students may choose to skip the lab course if they already have a strong programming background and are comfortable learning on their own using online resources and tutorials.

Required course background: probability and linear algebra; calc III and numerical optimization recommended. Recommended co-req: EN.601.382.

TuTh 12-1:15
limit 30

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
limit 10

601.690

NEW COURSE!

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
Sec 01: limit 10 (CS grad students or permission)

601.714

ADVANCED COMPUTER NETWORKS Ghorbani

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]
Recommended Course Background: One undergraduate course in computer networks (e.g., EN.601.414/614 Computer Network Fundamentals or the equivalent), or permission of the instructor. The course assignments and projects assume students to be comfortable with programming.

Pre-req: EN.601.414/614 or equivalent.

TuTh 1:30-2:45
limit 25

601.723

ADVANCED TOPICS IN DATA-INTENSIVE COMPUTING Burns

The advent of cloud computing has lead to an explosion of storage system and data analysis software, including NoSQL databases, bulk-synchronous processing, graph computing engines, and stream processing. This course will explore scale-out software architectures for data-processing tasks. It will examine the algorithms and data-structures that underlie scalable systems and look at how hardware and networking trends influence the design and deployment of cloud computing. Recommended Course Background: EN.600.320/420 or permission of instructor. [Systems]

Pre-req: 600.320/420 or equivalent.

TuTh 4:30-7p
limit 30

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.

tba
limit 30

601.801
600.601

COMPUTER SCIENCE SEMINAR

Required for all full-time PhD students. Recommended for MSE students.

TuTh 10:30-12
limit 90

601.803
600.803

MASTERS RESEARCH

Independent research for masters students. Permission required.

See below for faculty section numbers.

601.805
600.809

GRADUATE INDEPENDENT STUDY

Permission required.

See below for faculty section numbers.

601.807
600.807

TEACHING PRACTICUM Selinski

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
600.801

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

601.817
ADDED!

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
limit 14

601.826
600.726

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
limit 15

601.831
600.760

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
limit 30

601.845
NEW COURSE

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
limit 12

601.857
600.757

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.

tbd
limit 15

601.865
600.765

SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Eisner

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
limit 15

601.866
600.766

SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme

A seminar focussed on current research and survey articles on computational semantics.

Fr 10:45-11:45
limit 15

601.868
600.768

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
limit 15

601.875
600.775

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.

Thu 3-4:15

520.702

CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING Khudanpur

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
limit 80