601.104
600.104 (H)

COMPUTER ETHICS (1) Sheela Kosaraju

Students will examine a variety of topics regarding policy, legal, and moral issues related to the computer science profession itself and to the proliferation of computers in all aspects of society, especially in the era of the Internet. The course will cover various general issues related to ethical frameworks and apply those frameworks more specifically to the use of computers and the Internet. The topics will include privacy issues, computer crime, intellectual property law -- specifically copyright and patent issues, globalization, and ethical responsibilities for computer science professionals. Work in the course will consist of weekly assignments on one or more of the readings and a final paper on a topic chosen by the student and approved by the instructor.

Sec 01: Mon 4:30-6:30p, alternate weeks
Sec 02: Mon 6-8p, alternate weeks
limit 20 each, CS majors only

601.107
600.107 (E)

INTRO TO PROGRAMMING IN JAVA (3) Selinski

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.

Sec 01: MW 1:30-2:45, limit 120
Sec 02: MW 3-4:15, limit 80
Sec 03: MW 3-4:15, limit 40, CS freshmen only

601.108
600.108 (E)

INTRO PROGRAMMING LAB (1) Selinski

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 24
Sec 2: Thu 4:30-7:30p, limit 24
Sec 3: Fri 1:30-4:30p, limit 20

601.220
600.120 (E)

INTERMEDIATE PROGRAMMING (4) More/Amir

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, 600.107, 600.111, 600.112, 580.200 or equivalent.

CS/CE majors/minors only
Sec 01 (More): MWF 12:00-1:15
Sec 02 (More): MWF 1:30-2:45
Sec 03 (Amir): TuThFr 3:00-4:15 CS incoming freshmen only
Sec 04 (): MWF 4:30-5:45
limit 34/section

601.226
600.226 (E,Q)

DATA STRUCTURES (4) Froehlich/?

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 600.107 or 600.120 or equivalent.

Sec 01 (??): MWF 12-1:15, limit 75
Sec 02 (Froehlich): MWF 1:30-2:45, limit 75

601.229
600.233 (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, super-scalar 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. [Systems]

Prereq: 600.120. Students may receive credit for only one of 601.229, 600.233, 600.333 or 600.433.

MWF 10
limit 75

601.231
600.271 (E,Q)

AUTOMATA and COMPUTATION THEORY (3) More

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: 550.171.

TuTh 9-10:15
limit 75

601.255
600.255 (E)

INTRODUCTION TO VIDEO GAME DESIGN (3) Froehlich

A broad survey course in video game design (as opposed to mathematical game theory), covering artistic, technical, as well as sociological aspects of video games. Students will learn about the history of video games, archetypal game styles, computer graphics and programming, user interface and interaction design, graphical design, spatial and object design, character animation, basic game physics, plot and character development, as well as psychological and sociological impact of games. Students will design and implement an experimental video game in interdisciplinary teams of 3-4 students as part of a semester-long project.

Prereq: 601.220 and 601.226 or permission of instructor; Co-req: 601.256. Section 1 requires technical skills; section 2 requires artistic skills, including at least one multimedia course (preferably 2 or more).

MW 4:30-5:45
Sec 1: for technical students
Sec 2: for non-technical students
limit 20/section

601.256
600.256 (E)

INTRODUCTION TO VIDEO GAME DESIGN LAB (1) Froehlich

A lab course in support of 600.255: Introduction to Video Game Design covering a variety of multi-media techniques and applications from image processing, through sound design, to 3D modeling and animation. See 600.255: Introduction to Video Game Design for details about enrolling. Unlike in 600.255, the sections for the lab are meant to have a cross-section of students from different backgrounds. Ideally students working on a team project will be enrolled in the same lab section.

Co-req: 601.255.

Sec 1: M 6-9
Sec 2: T 6-9
Sec 3: W 6-9
Sec 4: H 6-9
limit 12/section

601.315
600.315 (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: 600.226. Students may receive credit for only one of 601.315/415/615.

TuTh 3-4:15
limit 30

601.318
600.318 (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, 600.226, and 600.233. Students may receive credit for only one of 601.318/418/618.

TuTh 9-10:15
limit 20

601.365
600.365 (E)

KNOWLEDGE DISCOVERY FROM TEXT (3) VanDurme & Lippincott

The world is full of text: webpages, emails, newspaper articles, tweets, medical records, and so on. The purpose of text is for people to convey knowledge to other people. This course focuses on how computers analyze large, potentially streaming, text collections to automatically discover knowledge on their own (and to help people better find it themselves). Lectures and assignments will cover relevant topics in automatic classification (applied machine learning), linguistics, high-performance computing, and systems engineering, working with software systems for automatic question answering, populating knowledge bases, and aggregate analysis of social media such as Twitter. [Applications]

Prereqs: 600.120 & 600.226.

TuTh 3-4:15
limit 30

601.415
600.415 (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, 600.226 and 600.233. Students may receive credit for only one of 601.318/418/618.

TuTh 9-10:15
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] (http://pl.cs.jhu.edu/oose/index.shtml)

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

MW 1:30-2:45
limit 64

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 or Perm. Req'd. Students may receive credit for only one of 601.433/633.

TuTh 1:30-2:45
limit 45

601.434
600.464 (E,Q)

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 and 550.310 or 550.420/620 or equivalent. Students may receive credit for only one of 601.434/634.

TuTh 12-1:15
limit 25

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) Akinyele & Wilson

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.

MF 4:30-5:45
limit 15

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, 600.226, 600.344/444 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.600.226 and EN.600.233. Students may receive credit for only one of 601.445/645.

MW 12-1:15
limit 25

601.447
600.439 (E)

COMPUTATIONAL GENOMICS: SEQUENCES (3) Langmead

Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, real-world genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming projects. [Applications]

Prereq: 600.120 & 600.226. Students may receive credit for at most one of 601.447/647/747.

TuTh 12-1:15
25

601.452
NEW COURSE! (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
limit 15

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: 600.226 and linear algebra, or permission. Recmd: 600.120, 600.457, 600.461, 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 (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 30

601.461
600.461 (E,Q)

COMPUTER VISION (3) Reiter

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 12-1:15
limit 35

601.463 ADDED!
600.436 (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

601.465
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: 600.226. 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, 600.226. 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 40

601.477
600.477 (E,Q)

ADDED!

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: 600.475/675 or stats/probability or permission. Students may receive credit for at most one of 601.477/677.

TuTh 3-4:15
limit 15

601.485
600.485 (Q)

PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille

[Co-listed with AS.050.375 and 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 25

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 (3) 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 20

601.618

OPERATING SYSTEMS (3) 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 9-10:15
limit 20

601.621
600.421 (E)

OBJECT ORIENTED SOFTWARE ENGINEERING (3) 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

601.633

INTRO ALGORITHMS Dinitz

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

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

TuTh 1:30-2:45
limit 45

601.634

RANDOMIZED & BIG DATA ALGORITHMS Braverman

Same material as 601.434, for graduate students. [Analysis] (www.cs.jhu.edu/~cs464)

Prereq: 600.363/463 and probability. Students may receive credit for only one of 601.434/634.

TuTh 12-1:15
limit 25

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 Akinyele & Wilson

Same material as 601.443, for graduate students. [Applications]

Required course background: A basic course in operating systems and networking, or permission of instructor.

MF 4:30-5:45
limit 30

601.644

NETWORK SECURITY (3) 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 (3) 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

601.647
600.639

COMPUTATIONAL GENOMICS: SEQUENCES Langmead

Same material as 601.447, for graduate students. [Applications]

Required Course Background: 600.120 & 600.226. Students may earn credit for at most one of 601.447/647/747.

TuTh 12-1:15
limit 25

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 (3) 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 25

601.661

COMPUTER VISION Reiter

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 12-1:15
limit 30

601.663 ADDED!

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

601.665

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 30

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 40

601.677

ADDED!

CAUSAL INFERENCE (3) 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

601.685

ADDED!

PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille

[Co-listed with AS.050.375 and 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

601.714

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]
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
600.624

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.

Tu 4:30-6, Th 4:30-8
limit 30

601.730
600.670

PSEUDORANDOMNESS AND COMBINATORIAL CONSTRUCTIONS Li

Randomness is very useful in almost all areas of computer science, such as algorithms, distributed computing and cryptography. However, computers generally do not have access to truly uniform random bits. To deal with this, we rely on various pseudorandom objects to reduce either the quantity or the quality of the random bits needed. In this course, we will develop provably good pseudorandom objects for a variety of tasks. We will frequently require explicit combinatorial constructions. That is, we will want to efficiently and deterministically construct such objects. Along the way, we will also explore the close connections of such objects to many other areas in computer science and mathematics, such as graph theory, coding theory, complexity theory and arithmetic combinatorics. [Analysis]

Required Course Background: 600.271/471, and probability.

TuTh 1:30-2:45
limit 25

601.747
600.639

COMPUTATIONAL GENOMICS: SEQUENCES Langmead

Similar material as 601.447/647, covered in more depth. [Applications]

Required Course Background: 600.120 & 600.226. Students may earn credit for at most one of 601.447/647/747.

Cancelled - merged with 601.647.

601.751
600.641
ADDED!

ADVANCED TOPICS IN GENOMIC DATA ANALYSIS Battle

[Formerly titled Machine Learning for Genomic Data - Trends and Applications]
Genomic data is becoming available in large quantities, but understanding how genetics contributes to human disease and other traits remains a major challenge. Machine learning and statistical approaches allow us to automatically analyze and combine genomic data, build predictive models, and identify genetic elements important to disease and cellular processes. This course will cover current uses of statistical methods and machine learning in diverse genomic applications including new genomic technologies. Students will present and discuss current literature. Topics include personal genomics, integrating diverse genomic data types, new technologies such as single cell sequencing and CRISPR, and other topics guided by student interest. The course will include a project component with the opportunity to explore publicly available genomic data. [Applications]

Recommended Course Background: coursework in data science or machine learning.

MW 9-10:15
limit 12

601.761
600.661

COMPUTER VISION Reiter

Similar material as 601.461/661, covered in more depth. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus)

Required course background: intro programming, linear algebra, prob/stat. Students may receive credit for at most one of 601.461/661/761.

Cancelled - merged with 601.661.

601.763
600.636

ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard

Similar material as 601.463/663, covered in more depth. [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.

Cancelled - merged with 601.663.

EN.601.775
EN.600.675

ADDED!

STATISTICAL MACHINE LEARNING Arora

This is a second graduate level course in machine learning. It will provide a formal and an in-depth coverage of topics at the interface of statistical theory and computational sciences. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall computational runtime (computation or iteration complexity). We will cover topics including nonparametric methods, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine-learning and randomized projections. Topics will vary from year-to-year but the general focus would be on combining methodology with theoretical and computational foundations. [Analysis or Applications]

Pre-req: 600.475/601.475/601.675 or equivalent or permission.

MW 3-4:15
limit 25

601.779
CANCELLED!

ADVANCED TOPICS IN REPRESENTATION LEARNING (3) Arora

This course will focus on recent advances in theory, methods and applications of representation learning. We will take a stochastic optimization view of representation learning, and explore various learning objectives and optimization algorithms for representation learning. In this stochastic optimization framework, we will present a unified view of different approaches to representation learning including function approximation, probabilistic, and information theoretic. We will study the optimization problems that result in each of these approaches and study algorithms for solving these optimization problems, both theoretically and empirically. Finally, we will discuss applications of these techniques to speech and language processing, computational healthcare, social media analytics, and computational neuroscience. [Analysis]

Pre-requisites: EN.600.479/679 Representation Learning or permission (requiring all of the following):

  1. Linear algebra (vector spaces, orthogonality, basis, 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)
  4. Convex optimization (differentiation, chain rule, unconstrained optimization)
  5. Machine learning (differentiation, chain rule, unconstrained optimization)

MW 3-4:15
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

601.826
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 Braverman, 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

601.833
600.764

SEMINAR IN ALGORITHMS Braverman

This course will explore algorithms and theoretical computer science with a focus on algorithms for massive data. Examples of topics include streaming algorithms, approximation algorithms, online algorithms. Students will be encouraged to select a paper and lead a discussion. External speakers will be invited to present current work as well. This course is a good opportunity for motivated students to learn modern algorithmic methods.

Prereq: 600.463 or equivalent.

offered?
limit 15

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
ADDED!

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

Faculty section numbers for all independent type courses, undergraduate and graduate.

01 - Xin Li
02 - Rao Kosaraju
03 - Yanif Ahmad
04 - Russ Taylor (ugrad research use 517, not 507)
05 - Scott Smith
06 - Joanne Selinski
07 - Harold Lehmann
08 - 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 - Andreas Terzis
24 - Raman Arora (ugrad research use 517, not 507)
25 - Rai Winslow
26 - Misha Kazhdan
27 - Chris Callison-Burch
28 - Peter Froehlich
29 - Alex Szalay
30 - Peter Kazanzides
31 - Jerry Prince
32 - Rajesh Kumar
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 - Joel Bader
38 - Ben VanDurme
39 - Jeff Siewerdsen
40 - Vladimir Braverman
41 - Suchi Saria
42 - Ben Langmead
43 - Steven Salzberg
44 - [ Stephen Checkoway ]
45 - Liliana Florea
46 - Adam Lopez
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
59 - Kevin Duh