Below are the computer science course offerings for one semester. This list only includes courses that count without reservation towards CS program requirements. Undergraduate majors might also want to consult the list of nondepartment courses that may be used as "CS other" in accordance with established credit restrictions.
All undergraduate courses except EN.500.112 will initially be listed as CS/CE majors/minors only. All graduate courses will initially be listed as CS grads only, with a few exceptions. After the initial registration period for each group these restrictions will be lifted (on or about 11/15 for ugrad courses, 11/30 for grad courses). [Some courses have sections explicitly for nonCS students, but instructor permission will be required.]
NOTE NEW COURSE NUMBERS  In order to be compliant with undergraduate students only in courses <=5xx and graduate students in courses >=6xx, we have completely renumbered all the courses in the department, with a 601 prefix instead of the old 600 prefix. Courses are listed here with both numbers  note that some suffixes have also changed as noted in bold. Grad students must take courses 601.6xx and above to count towards their degrees going forward. Combined bachelors/masters students may count courses numbered 600.4xx towards their masters degree if taken before the undergrad degree was completed. [All colisted 601.4xx/6xx courses are equivalent.]
Courses without end times are assumed to meet for 50 minute periods. Final room assignments will be available on the Registrar's website in January. Changes to the original SISposted schedule are noted in red.
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 objectoriented 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. See SIS class search for sections. 
MWF 50 minutes, limit 19/section 
601.104 (600.104) (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 4:306:30, alternate weeks (start 1/30)

601.105 (600.105) 
M&Ms: CS FRESHMAN EXPERIENCE (1) Selinski This course provides freshmen computer science majors with an introduction to the field and department. A variety of faculty members will provide a mix of historical context and current topics. Classes will be interactive, enabling students to think about and explore topics in a fun way, as well as get to know their classmates. CS nonfreshmen and minors may enroll by permission only. Satisfactory/Unsatisfactory only. 
cancelled 
601.220 (600.120) (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 lowlevel programming techniques, as well as objectoriented 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: C+ or better grade in AP CS, 601.107, 600.112, 500.112, 580.200 or equivalent. 
Sec 01 (): MWF 121:15 
601.226 (600.226) (E,Q) 
DATA STRUCTURES (4) Selinski 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: C+ or better grade in 500.112, 601.107, or 601.220. 
MWF 121:15 
601.229 (600.233) (E) 
COMPUTER SYSTEM FUNDAMENTALS (3) Pouliquen We study the design and performance of a variety of computer systems from simple 8bit microcontrollers through 32/64bit 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: 601.220. 
MWF 1:30 
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, NPcompleteness, and randomization. Students may not receive credit for 600.271/601.231 and 600.471/601.631 for the same degree. Prereq: 553.171/172. 
TuTh 1:302:45

601.290 (600.250) (E) 
USER INTERFACES AND MOBILE APPLICATIONS (3) Selinski This course will provide students with a rich development experience, focused on the design and implementation of user interfaces and mobile applications. A brief overview of human computer interaction will provide context for designing, prototyping and evaluating user interfaces. Students will invent their own mobile applications and implement them using the Android SDK, which is JAVA based. An overview of the Android platform and available technologies will be provided, as well as XML for layouts, and general concepts for effective mobile development. Students will be expected to explore and experiment with outside resources in order to learn technical details independently. There will also be an emphasis on building teamwork skills, and on using modern development techniques and tools. [Oral] Prereq: 601.220 and 601.226. 
TuTh 34:15 
601.295(E) 
DEVELOPING HEALTH IT APPLICATIONS (3) Shpitser & Overby This course is a projectbased introduction to working on successful projects in health care. In the first half of the term, students perform reading and homework assignments designed to introduce: (1) the context of health care delivery and health IT, (2) techniques to overcome challenges to conducting health care data analyses, and (3) techniques to design meaningful applications around health care data. In the second half of the term, students work in small groups to solve a realworld problem of their choosing. Includes exercises in written and oral communication and team building. [Oral starting 2019] Prereq: 601.220 and 601.226. 
TuTh 910:15 
601.320 (600.320) (E) 
PARALLEL PROGRAMMING (3) Burns This course prepares the programmer to tackle the massive data sets and huge problem size of modern scientific and enterprise computing. Google and IBM have commented that undergraduate CS majors are unable to "break the single server mindset" (http://www.google.com/intl/en/ press/pressrel/20071008_ibm_univ.html). Students taking this course will abandon the comfort of serial algorithmic thinking and learn to harness the power of cuttingedge software and hardware technologies. The issue of parallelism spans many architectural levels. Even ``single server'' systems must parallelize computation in order to exploit the inherent parallelism of recent multicore processors. The course will examine different forms of parallelism in four sections. These are: (1) massive dataparallel computations with Hadoop!; (2) programming compute clusters with MPI; (3) threadlevel parallelism in Java; and, (4) GPGPU parallel programming with NVIDIA's Cuda. Each section will be approximately 3 weeks and each section will involve a programming project. The course is also suitable for undergraduate and graduate students from other science and engineering disciplines that have prior programming experience. [Systems] Prereq: 601.226 and 601.299. Students may receive credit for at most one of 601.320/420/620. 
MW 4:305:45 
601.356 (600.452) (E) 
COMPUTER INTEGRATED SURGERY SEMINAR (1) Taylor Lecture only version of 601.456 (no project). Prereq: 601.455 or perm req'd. Students may receive credit for 600.356 or 600.456, but not both. 
TuTh 1:302:45 
601.382 (E) 
DEEP LEARNING LAB (1) Unberath This course is an optional handson 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. Coreq: EN.601.482 or EN.601.682 or EN.601.765. 
Tu 4:306:30 
601.402 (600.402) (E) 
DIGITAL HEALTH AND BIOMEDICAL INFORMATICS (1) Lehmann Advances in technology are driving a change in medicine, from personalized medicine to population health. Computers and information technology will be critical to this transition. We shall discuss some of the coming changes in terms of computer technology, including computerbased patient records, clinical practice guidelines, and regionwide health information exchanges. We will discuss the underlying technologies driving these developments  databases and warehouses, controlled vocabularies, and decision support. Prerequisite: none. Short course meets 4 weeks: Feb 4  Feb 27 
MW 4:305:45 
601.411 (600.411) (E) 
CS INNOVATION AND ENTREPRENEURSHIP II (3) Dahbura & Aronhime This course is the second half of a twocourse sequence and is a continuation of course 660.410.01, CS Innovation and Entrepreneurship, offered by the Center for Leadership Education (CLE). In this sequel course the student groups, directed by CS faculty, will implement the business idea which was developed in the first course and will present the implementations and business plans to an outside panel made up of practitioners, industry representatives, and venture capitalists. [Oral] Prerequisite: 660.410. 
Thu 36p 
601.414 (600.444) (E) 
COMPUTER NETWORKS (3) Jin Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on handson programming assignments, homeworks and two exams. [Systems] Prerequisites: EN.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. 
MW 34:15 
601.419 (E) New Course! 
CLOUD COMPUTING (3) Ghorbani Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and lowlatency requirements of realtime applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. [Systems] Prerequisites: EN.601.226 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming. 
MW 121:15 
601.420 (600.420) (E) 
PARALLEL PROGRAMMING (3) Burns More advanced version of 601.320. Students may receive credit for at most one of 601.320/420/620. [Systems]
Required course background: 601.226 and 601.229 or equiv. 
MW 4:305:45 
601.426 (600.426) (E,Q) 
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Functional, objectoriented, and other language features are studied independent of a particular programming language. Students become familiar with these features by implementing them. Most of the implementations are in the form of small language interpreters. Some type checkers and a small compiler will also be written. The total amount of code written will not be overly large, as the emphasis is on concepts. The ML programming language is the implementation language used. [Analysis] Required course background: 601.226. Freshmen and sophomores by permission only. 
MW 1:302:45 
601.433 (600.463) (E,Q) 
INTRO ALGORITHMS (3) Kosaraju
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 unionfind); graph algorithms and searching techniques such as minimum spanning trees, depthfirst search, shortest paths, design of online algorithms and competitive analysis. [Analysis] Prereq: 601.226 and (553.171/172 or 601.231) or Perm. Req'd. Students may receive credit for only one of 601.433/633. 
TuTh 121:15 
601.435 (600.469) (E,Q) 
APPROXIMATION ALGORITHMS (3) Dinitz This course provides an introduction to approximation algorithms. Topics include vertex cover, TSP, Steiner trees, cuts, greedy approach, linear and semidefinite programming, primaldual method, and randomization. Additional topics will be covered as time permits. There will be a final project. [Analysis] Prereq: 601.433/633 or permission. Students may receive credit for only one of 601.435/635. 
TuTh 34:15 
601.441 (600.451) (E)

BLOCKCHAINS AND CRYPTOCURRENCIES Jain & Green This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. [Analysis] Students may receive credit for only one of 600.451, 601.441, 601.641. Prereq: 601.226 and (EN.553.310 or EN.553.410 probability). 
MW 1:302:45 
601.444 
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 34:15 
601.448 (600.438) (E)

COMPUTATIONAL GENOMICS: DATA ANALYSIS (3) Battle [Crosslisted in BME.] Genomic data has the potential to reveal causes of disease, novel drug targets, and relationships among genes and pathways in our cells. However, identifying meaningful patterns from highdimensional genomic data has required development of new computational tools. This course will cover current approaches in computational analysis of genomic data with a focus on statistical methods and machine learning. Topics will include disease association, prediction tasks, clustering and dimensionality reduction, data integration, and network reconstruction. There will be some programming and a project component. [Applications] Required Course Background: 601.220 or equivalent programming experience; probability and statistics; linear algebra and calculus. Students may receive credit for only one of 601.448/648. 
MW 121:15 
601.454 (600.484) (E) 
AUGMENTED REALITY (3) Navab
Same as 601.654, for undergraduate students.
[Applications] Students may receive credit for only one of 601.454/654. Prerequisites: EN.601.220, EN.601.226, linear algebra. 
TuTh 910:15 
601.456 (600.446) (E) 
COMPUTER INTEGRATED SURGERY II (3) Taylor This weekly lecture/seminar course addresses similar material to 600.455, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.455, although it does not have to be. Grades are based both on the project and on classroom recitations. Students wishing to attend the weekly lectures as a 1credit seminar should sign up for 601.356. [Applications, Oral]
Prereq: 601.455/655 or perm req'd. Students may receive credit for
601.456 or 601.656, but not both. 
TuTh 1:302:45 
601.463 (600.436) (E) 
ALGORITHMS FOR SENSORBASED 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 humanmachine systems. [Analysis] Prereq: 601.226, linear algebra, probability. Students may receive credit for only one of 601.463/663. 
TuTh 121:15 
601.464 (600.435) (E) 
ARTIFICIAL INTELLIGENCE (3) Koehn The course situates the study of Artificial Intelligence (AI) first in the broader context of Philosophy of Mind and Cognitive Psychology and then treats indepth methods for automated reasoning, automatic problem solvers and planners, knowledge representation mechanisms, game playing, machine learning, and statistical pattern recognition. The class is a recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as deduction, learning, and planning and navigation. Strong programming skills and a good grasp of the English language are expected; students will be asked to complete both programming assignments and writing assignments. The course will include a brief introduction to scientific writing and experimental design, including assignments to apply these concepts. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 
TuTh 1:302:45 
601.466 (600.466) (E) 
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky An indepth, handson study of current information retrieval techniques and their application to developing intelligent WWW agents. Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modeling, information visualization and usage pattern analysis. In addition, the course explores the range of additional language processing steps useful for template filling and information extraction from retrieved documents, focusing on recent, primarily statistical methods. The course concludes with a study of current issues in information retrieval and data mining on the World Wide Web. Topics include web robots, spiders, agents and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. [Applications] Required course background: 601.226. 
TuTh 34:15 
601.475 (600.475) (E) 
MACHINE LEARNING (3) Arora
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 prerequisites that include probability, linear
algebra, multivariate calculus and basic optimization. Students in
the course will develop a learning system for a final project.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Required course background: multivariable calculus, probability, linear algebra. 
TuThFr 34:15

601.476 (600.476) (EQ) 
MACHINE LEARNING: DATA TO MODELS (3) Malinsky How can robots localize themselves in an environment when navigating? Which factors predict whether patients are at greatestrisk for complications in the hospital? Can we reconstruct the brain's "connectome" from fMRI data? Many such big data questions can be answered using the paradigm of probabilistic models in machine learning. This is a second course on machine learning which focuses on probabilistic graphical models. You will learn about directed and undirected graphical models, inference methods, sampling, structure learning algorithms, latent variables, and temporal models. There will be regular assignments, which include theory and some programming. Students will analyze real data for their final project, applying methods discussed in class and writing up a report of their results. [Analysis or Applications] Students may receive credit for 600.476 or 600.676, but not both. Prereqs: EN.600/601.475/675 or equivalent. 
TuTh 121:15 
601.482 (E) 
MACHINE LEARNING: DEEP LEARNING (3) Unberath
Deep learning (DL) has emerged as a powerful tool for solving
dataintensive 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 perceptionbased
robotics. Prereq: (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); numerical optimization recommended. Recommended coreq: EN.601.382. 
TuTh 121:15 
EN.580.488 (600.488) (E) 
FOUNDATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS II Karchin [BME crosslist, counts as CS upper level credit.] This course will introduce probabilistic modeling and information theory applied to biological sequence analysis, focusing on statistical models of protein families, alignment algorithms, and models of evolution. topics will include probability theory, score matrices, hidden Markov models, maximum likelihood, expectation maximization and dynamic programming algorithms. Homework assignments will require programming in Python. Foundations of Computational Biology I is not a prereq. [Analysis] Required course background: math through linear algebra and differential equations, at least one statistics and probability course, 580.221 or equiv., 601.226 or equiv. 
MW 4:305:45 
601.491 (E) 
HUMANROBOT INTERACTION (3) C. Huang This course is designed to introduce advanced students to research methods and topics in humanrobot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledge of various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. [Applications] Prerequisite: EN.601.220 and EN.601.226. 
TuTh 34:15 
601.501 (600.592) 
COMPUTER SCIENCE WORKSHOP An independent applicationsoriented, 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. Permission of faculty sponsor is required. 
See below for faculty section numbers. 
601.503 (600.504) 
UNDERGRADUATE INDEPENDENT STUDY Individual guided study for undergraduates, 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 of faculty sponsor is required.

See below for faculty section numbers. 
601.507 (600.508) 
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 508 or 518. 
601.509 (600.510) 
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study 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, which is the limit per semester. Permission of faculty sponsor is required. 
See below for faculty section numbers. 
601.517 (600.518) 
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. 
601.520 (600.520) 
SENIOR HONORS THESIS (3) For computer science majors only, a continuation of 601.519. Prerequisite: 601.519 
See below for faculty section numbers. 
601.611 (600.411) 
CS INNOVATION AND ENTREPRENEURSHIP II Dahbura & Aronhime Graduate level version of EN.601.411 (see for description) Prerequisites: 660.410. 
Tue 4:307p 
601.614 (600.444) 
COMPUTER NETWORKS Jin Same as 601.414, for graduate students. [Systems] Required course background: EN.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. 
MW 34:15 
601.619 New Course! 
CLOUD COMPUTING Ghorbani [Same as 601.419, for graduate students.] Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and lowlatency requirements of realtime applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. [Systems] Prerequisites: EN.601.226 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming. 
MW 121:15 
601.620 (600.420) 
PARALLEL PROGRAMMING (3) Burns Same as 601.420, for graduate students. Students may receive credit for at most one of 601.320/420/620. [Systems]
Required course background: 601.226 and 601.229 or equiv. 
MW 4:305:45 
601.626 (600.426) 
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Same as 601.426, for graduate stuents. Students may receive credit for only one of 601.426/626. [Analysis] Required course background: 601.226. 
MW 1:302:45 
601.631 (600.471) 
THEORY OF COMPUTATION Li This is a graduatelevel 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. 
TuTh 121:15 
601.633 (600.463) 
INTRO ALGORITHMS Kosaraju
Same as 601.433, for graduate students. [Analysis] Prereq: 601.226 and 553.171/172 or Perm. Req'd. Students may receive credit for only one of 601.433/633. 
TuTh 121:15 
601.635 (600.469) 
APPROXIMATION ALGORITHMS Dinitz Same as 601.435, for graduate students. [Analysis] Required course background: 601.433/633 or permission. Students may receive credit for only one of 601.435/635. 
TuTh 34:15 
601.641 (600.451)

BLOCKCHAINS AND CRYPTOCURRENCIES Jain & Green [Crosslisted in JHUISI.] Same as EN.601.441, for graduate students. [Analysis] Students may receive credit for only one of 600.451, 601.441, 601.641. Required course background: 601.226 and probability (any course). 
MW 1:302:45 
601.644 
NETWORK SECURITY Nielson [Crosslisted 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 34:15 
601.648 (600.638) 
COMPUTATIONAL GENOMICS: DATA ANALYSIS (3) Battle [Crosslisted in BME.] Graduate version of 601.448. Students may receive credit for only one of 600.338/601.448/600.638/601.648. [Applications] Recommended Course Background: 601.220 or equivalent programming experience; probability and statistics; linear algebra and calculus. Students may receive credit for only one of 601.448/648. 
MW 121:15 
601.654 (600.684) 
AUGMENTED REALITY (3) Navab
This course introduces students to the field of Augmented
Reality. It reviews its basic definitions, principles and
applications. It then focuses on Medical Augmented Reality and its
particular requirements. The course also discusses the main issues of
calibration, tracking, multimodal registration, advance visualization
and display technologies. Homework in this course will relate to the
mathematical methods used for calibration, tracking and visualization
in medical augmented reality. Students may also be asked to read
papers and implement various techniques within group
projects. [Applications] Students may receive credit for 600.484 or 600.684, but
not both. Required course background: intermediate programming (C/C++), data structures, linear algebra. 
TuTh 910:15 
601.656 (600.646) 
COMPUTER INTEGRATED SURGERY II Taylor Same as 601.456, for graduate students. [Applications] Prereq: 601.455/655 or perm req'd. Students may receive credit for only one of 601.456/656. 
TuTh 1:302:45 
601.663 (600.636) 
ALGORITHMS FOR SENSORBASED ROBOTICS Leonard Same as 601.463, for graduate students. [Analysis] Required course background: 601.226, calculus, prob/stat. Students may receive credit for only one of 601.463/663. 
TuTh 121:15 
601.664 (600.435) 
ARTIFICIAL INTELLIGENCE Koehn 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 1:302:45 
601.666 (600.466) 
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky Same material as 601.466, for graduate students. [Applications] Students may receive credit for at most one of 601.466/666. Required course background: 601.226. 
TuTh 34:15 
601.675 (600.475) 
MACHINE LEARNING Arora
Same as 601.475, for graduate students.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Required course background: multivariable calculus, probability, linear algebra. 
TuThFr 34:15

601.676 (600.676) 
MACHINE LEARNING: DATA TO MODELS Malinsky Same as 601.476, for graduate students. [Analysis or Applications] Students may receive credit for only one of 601.476/676. Prereqs: EN.600/601.475/675 or equivalent. 
TuTh 121:15 
601.682 
MACHINE LEARNING: DEEP LEARNING (3) Unberath
Same as 601.482, for graduate students. [Applications]
Required course background: probability and linear algebra; numerical optimization recommended. Recommended coreq: EN.601.382. 
TuTh 121:15 
580.688 
FOUNDATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS II Karchin [Crosslisted from BME  CS students can count as a CS course.] This course will introduce probabilistic modeling and information theory applied to biological sequence analysis, focusing on statistical models of protein families, alignment algorithms, and models of evolution. topics will include probability theory, score matrices, hidden Markov models, maximum likelihood, expectation maximization and dynamic programming algorithms. Homework assignments will require programming in Python. Foundations of Computational Biology I is not a prereq. [Analysis] Required course background: math through linear algebra and differential equations, at least one statistics and probability course, 580.221 or equiv., 601.226 or equiv. 
MW 4:305:45 
601.691 
HUMANROBOT INTERACTION C. Huang This course is designed to introduce graduate students to research methods and topics in humanrobot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledge of various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. [Applications] Required course background: EN.601.220 and EN.601.226. 
TuTh 34:15 
601.743 (600.643) 
ADVANCED TOPICS IN COMPUTER SECURITY Rubin [Crosslisted in ISI] Topics will vary from year to year, but will focus mainly on network perimeter protection, hostlevel protection, authentication technologies, intellectual property protection, formal analysis techniques, intrusion detection and similarly advanced subjects. Emphasis in this course is on understanding how security issues impact real systems, while maintaining an appreciation for grounding the work in fundamental science. Students will study and present various advanced research papers to the class. There will be homework assignments and a course project. [Systems or Applications] Prereq: 600.443/601.443/601.643 or 600.424/601.444/601.644; or permission of instructor. 
TuTh 910:15 
601.745 
ADVANCED TOPICS IN APPLIED CRYPTOGRAPHY (3) Green [Crosslisted 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. 
CANCELLED 
601.749 (600.649) 
COMPUTATIONAL GENOMICS: APPLIED COMPARATIVE GENOMICS (3) Schatz The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm & final exam, class presentations, and a significant class project. [Applications] Prereq: familiarity with UNIX scripting and/or programming. 
MW 1:302:45 
EN.601.760 (600.660)

FFT IN GRAPHICS & VISION (3) Kazhdan In this course, we will study the Fourier Transform from the perspective of representation theory. We will begin by considering the standard transform defined by the commutative group of rotations in 2D and translations in two and threedimensions, and will proceed to the Fourier Transform of the noncommutative group of 3D rotations. Subjects covered will include correlation of images, shape matching, computation of invariances, and symmetry detection. [Applications or Analysis] Prereq: linear algebra and comfort with mathematical derivations. 
MW 1:302:45 
601.765 
MACHINE LEARNING: LINGUISTIC & SEQUENCE MODELING Eisner This course surveys formal ingredients that are used to build structured models of character and word sequences. We will unpack recent deep learning architectures that consider various kinds of latent structure, and see how they draw on earlier work in structured prediction, dimensionality reduction, Bayesian nonparametrics, multitask learning, etc. We will also examine a range of strategies used for inference and learning in these models. Students will be expected to read recent papers and carry out a research project. [Applications or Analysis] Prerequisites: EN.600/601.465/665 or permission. Prior coursework in statistics or machine learning is recommended. Students may wish to prepare for their choice of research project by taking EN.601.382 Deep Learning Lab at the same time. 
MWF 34:15 
601.783 (600.683) 
VISION AS BAYESIAN INFERENCE (3) Yuille This is an advanced course on computer vision from a probabilistic and machine learning perspective. It covers techniques such as linear and nonlinear filtering, geometry, energy function methods, markov random fields, conditional random fields, graphical models, probabilistic grammars, and deep neural networks. These are illustrated on a set of vision problems ranging from image segmentation, semantic segmentation, depth estimation, object recognition, object parsing, scene parsing, action recognition, and text captioning. [Analysis or Applications] Required course background: calculus, linear algebra (AS.110.201 or equiv.), probability and statistics (AS.550.311 or equiv.), and the ability to program in Python and C++. Background in computer vision (EN.601.461/661) and machine learning (EN.601.475/675) suggested but not required. 
TuTh 910:15 
601.801 (600.602) 
Required for all CS PhD students. Strongly recommended for MSE students. 
TuTh 10:3012 
601.803 (600.804) 
MASTERS RESEARCH Independent research for masters or predissertation PhD students. Permission required. 
See below for faculty section numbers. 
601.805 (600.810) 
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 (600.802) 
PHD RESEARCH 
See below for faculty section numbers. 
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 faulttolerance, reliability, verification, energy efficiency, and virtualization. 
Fr 12:15 
601.826 (600.726) 
SELECTED TOPICS IN PROGRAMMING LANGUAGES Smith This seminar course covers recent developments in the foundations of programming language design and implementation. topics covered include type theory, process algebra, higherorder program analysis, and constraint systems. Students will be expected to present papers orally. 
Fri 12:30 
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 
601.856 (600.746) 
SEMINAR: MEDICAL IMAGE ANALYSIS Taylor & Prince [Colisted as 520.746] This weekly seminar will focus on research issues in medical image analysis, including image segmentation, registration, statistical modeling, and applications. It will also include selected topics relating to medical image acquisition, especially where they relate to analysis. The purpose of the course is to provide the participants with a thorough background in current research in these areas, as well as to promote greater awareness and interaction between multiple research groups within the University. The format of the course is informal. Students will read selected papers. All students will be assumed to have read these papers by the time the paper is scheduled for discussion. But individual students will be assigned on a rotating basis to lead the discussion on particular papers or sections of papers. Colisted with 520.746. 
Tu 34:50 
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. 
Malone 229 
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. Required course background: 600.465 or permission of instructor. 
Wed 121:15 
601.866 (600.766) 
SELECTED TOPICS IN MEANING, TRANSLATION AND GENERATION OF TEXT VanDurme & Rawlins (?) A seminar focussed on current research and survey articles on computational semantics. 
Fr 1010:50 
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. 
T 9:3010:30 
500.745 
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Cowan, Whitcomb, Vidal, EtienneCummings Seminar series in robotics. Topics include: Medical robotics, including computerintegrated surgical systems and imageguided 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. Humanmachine systems, including haptic and visual feedback, human perception, cognition and decision making, and humanmachine collaborative systems. Crosslisted with Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering. 
Wed 121:30 
520.702 
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING Trmal CLSP seminar series, for any students interested in current topics in language and speech processing. 
Mon & Fri 121:15 
530.707 
ROBOT SYSTEM PROGRAMMING Whitcomb (see SIS) 
TuTh 4:305:45 
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  [John Sheppard] 09  Greg Hager (ugrad research use 517, not 507) 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 CallisonBurch 28  Ali Darvish [Froehlich] 29  Alex Szalay 30  Peter Kazanzides 31  Jerry Prince 32  Carey Priebe [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  Mathias Unberath [Joel Bader] 38  Ben VanDurme 39  Jeff Siewerdsen 40  Vladimir Braverman 41  Suchi Saria 42  Ben Langmead 43  Steven Salzberg 44  Haider Ali 45  Liliana Florea 46  Casey Overby Taylor [Adam Lopez] 47  Philipp Koehn 48  Abhishek Jain 49  Anton Dahbura (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  ChienMing Huang 58  Will Gray (ugrad research use 517, not 507) 59  Kevin Duh 60  [Marin Kobilarov]