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: M 4:306:30, alternate weeks (start 1/29)

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. 
Tu 4:30 
601.107 (600.107) (E) 
INTRODUCTORY PROGRAMMING IN JAVA (3) More This course introduces fundamental structured and objectoriented 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 operators, control structures, arrays, functions, recursion, dynamic memory allocation, files, class usage and class writing. Program design and testing are also covered, in addition to more advanced objectoriented concepts including inheritance and exceptions as time permits. Firsttime programmers are strongly advised to take 601.108 concurrently in Fall/Spring semesters. Prereq: familiarity with computers. Students may receive credit for one of 601.107, 600.112, 580.200. 
MW 1:302:45 
601.108 (600.108) (E) 
INTRO PROGRAMMING LAB (1) More Satisfactory/Unsatisfactory only. This course is intended for novice programmers, and must be taken in conjunction with 600.107. The purpose of this course is to give firsttime programmers extra handson practice with guided supervision. Students will work in pairs each week to develop working programs, with checkpoints for each development phase. Prerequisite: familiarity with computers. CoRequisite: 601.107. 
Sec 1: Wed 69p, limit 24 
601.220 (600.120) (E)

INTERMEDIATE PROGRAMMING (4) More/Kazhdan/Langmead 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: AP CS, 601.107, 600.112, 580.200 or equivalent. 
Sec 01 (More): MWF 121:15 
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: 601.107 or 601.220. 
MWF 121:15, limit 125 (CS/CE major/minor to start) 
601.229 (600.233) (E) 
COMPUTER SYSTEM FUNDAMENTALS (3) Koehn 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. [Systems] Prereq: 601.220. 
MWF 1:30, limit 75 
601.231 (600.271) (E,Q) 
AUTOMATA and COMPUTATION THEORY (3) Li This course is an introduction to the theory of computing. topics include design of finite state automata, pushdown automata, linear bounded automata, Turing machines and phrase structure grammars; correspondence between automata and grammars; computable functions, decidable and undecidable problems, P and NP problems, NPcompleteness, and randomization. Students may not receive credit for 600.271 and 600.471 for the same degree. Prereq: 553.171. 
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. Prereq: 601.120 and 601.226. 
TuTh 34:15 
601.295(E) NEW COURSE! 
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. Prereq: 601.120 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 
601.328 (600.328) (E) 
COMPILERS & INTERPRETERS (3) Froehlich Introduction to compiler design, including lexical analysis, parsing, syntaxdirected translation, symbol tables, runtime environments, and code generation and optimization. Students are required to write a compiler as a course project. [Systems] Prereq: 601.220, 601.226, 601.229; 601.231 recommended. Students may receive credit for at most one of 601.328/428/628. 
MWF 10 
601.350 (600.340) (E) 
INTRODUCTION TO GENOMIC RESEARCH (3) Salzberg This course will use a projectbased approach to introduce undergraduates to research in computational biology and genomics. During the semester, students will take a series of large data sets, all derived from recent research, and learn all the computational steps required to convert raw data into a polished analysis. Data challenges might include the DNA sequences from a bacterial genome project, the RNA sequences from an experiment to measure gene expression, the DNA from a human microbiome sequencing experiment, and others. Topics may vary from year to year. In addition to computational data analysis, students will learn to do critical reading of the scientific literature by reading highprofile research papers that generated groundbreaking or controversial results. [Applications] Prerequisites: knowledge of the Unix operating system and programming expertise in a language such as Perl or Python.

TuTh 34:15 
601.355 (600.355) (E) 
VIDEO GAME DESIGN PROJECT (3) Froehlich An intensive capstone design project experience in video game development. Students will work in groups of 48 on developing a complete video game of publishable quality. Teams will (hopefully) include programmers, visual artists, composers, and writers. Students will be mentored by experts from industry and academia. Aside from the project itself, project management and communication skills will be emphasized. Enrollment is limited to ensure parity between the various disciplines. [General] May involve travel to MICA. Prereq: 601.255 or permission of instructor; junior or senior standing recommended. 
Wed 4:307:30p 
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) NEW COURSE! 
DEEP LEARNING LAB (1) Hager & DiPietro 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: Jan 29  Feb 21 
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. [General] Prerequisites: 660.410 and 601.421/621. 
Tue 4:307p 
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.601.120 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. 
TuTh 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 
601.428 (600.428) (E) 
COMPILERS & INTERPRETERS (3) Froehlich More advanced version of 600.328. Students may receive credit for only one of 601.328/428/628. [Systems] Prereq: 600.120 & 600.226 
MWF 10 
601.433 (600.463) (E,Q) 
INTRO ALGORITHMS (3) Braverman
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 or Perm. Req'd. Students may receive credit for only one of 601.433/633. 
TuTh 121:15 
601.436 (600.473) (E,Q) 
ALGORITHMIC GAME THEORY Dinitz This course provides an introduction to algorithmic game theory: the study of games from the perspective of algorithms and theoretical computer science. There will be a particular focus on games that arise naturally from economic interactions involving computer systems (such as economic interactions between largescale networks, online advertising markets, etc.), but there will also be broad coverage of games and mechanisms of all sorts. Topics covered will include a) complexity of computing equilibria and algorithms for doing so, b) (in)efficiency of equilibria, and c) algorithmic mechanism design. Students may receive credit for 601.436 or 601.636, but not both. Prereq: 601.433/633 or permission. [Analysis] Prereq: 600.433/633 or permission. 
TuTh 34:15 
601.441 (600.451) (E) NEW COURSE! 
BLOCKCHAINS AND CRYPTOCURRENCIES Jain 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 
601.448 (600.438) (E) CANCELLED 
COMPUTATIONAL GENOMICS: DATA ANALYSIS (3) Battle 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] Recommended Course Background: 601.226 or other programming experience, probability and statistics, linear algebra or calculus. Students may receive credit for only one of 601.448/648. 
CANCELLED 
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.601.120, 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]
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) VanDurme 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) TENTATIVE 
MACHINE LEARNING (3) staff
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. 
MWF 4:305:45 TENTATIVE

601.476 (600.476) (EQ) 
MACHINE LEARNING: DATA TO MODELS (3) Saria How can robots localize themselves in an environment when navigating? Can we predict which patients are at greatestrisk for complications in the hospital? Which movie should I recommend to this user given his history of likes? Many such big data questions can be answered using the paradigm of probabilistic models in machine learning. These are especially useful when common offtheshelf algorithms such as support vector machines and kmeans fail. You will learn methods for clustering, classification, structured prediction, recommendation and inference. We will use Murphy's book, Machine Learning: a Probabilistic Perspective, as the text for this course. Assignments are solved in groups of size 13 students. The class will have 4 interactive sessions during which we brainstorm how to solve example openended realworld problems with the tools learnt in class. Students are also required to do a project of their choice within which they experiment with the ideas learnt in class. [Analysis or Applications] Students may receive credit for 600.476 or 600.676, but not both. Prereqs: 1) Intro Prob/Stat, Linear Algebra and Intro Machine Learning OR 2) Strong background in statistics (at least 12 upper level classes in statistics) and programming (fluency with ideally Python and in the very least R/Matlab). 
TuTh 4:305:45 
601.479 (600.479) (E) NOT OFFERED 
REPRESENTATION LEARNING (3) Arora
Often the success of a machine learning project depends on the choice
of features used. Machine learning has made great progress in
training classification, regression and recognition systems when
"good" representations, or features, of input data are
available. However, much human effort is spent on designing good
features which are usually knowledgebased and engineered by domain
experts over years of trial and error. A natural question to ask then
is "Can we automate the learning of useful features from raw
data?" Representation learning algorithms such as principal component
analysis aim at discovering better representations of inputs by
learning transformations of data that disentangle factors of variation
in data while retaining most of the information. The success of such
datadriven approaches to feature learning depends not only on how
much data we can process but also on how well the features that we
learn correlate with the underlying unknown labels (semantic content
in the data). This course will focus on scalable machine learning
approaches for learning representations from large amounts of
unlabeled, multimodal, and heterogeneous data. We will cover topics
including deep learning, multiview learning, dimensionality
reduction, similaritybased learning, and spectral learning.
[Analysis or Applications]
Required course background: machine learning or basic probability and linear algebra; mathematical maturity. 
?? 
601.482 (E) NEW COURSE! 
MACHINE LEARNING: DEEP LEARNING (3) Hager
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 
601.488 (600.488) (E) 
FOUNDATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS II Karchin [Colisted with 580.488/688.] 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 
601.501 (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. 
601.556 (600.546) 
SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3) Prereq: 60.455 or perm req'd. 
Section 01: Taylor 
601.611 (600.411) 
CS INNOVATION AND ENTREPRENEURSHIP II (3) Dahbura & Aronhime Graduate level version of EN.601.411 (see for description) Prerequisites: 660.410 and 601.421/621. 
Tue 4:307p 
601.614 (600.444) 
COMPUTER NETWORKS (3) Jin Same as 601.414, for graduate students. [Systems] Required course background: EN.601.120 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. 
TuTh 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 
601.628 (600.428) 
COMPILERS & INTERPRETERS (3) Froehlich Same as 600.428, for graduate students. Students may receive credit for only one of 601.328/428/628. [Systems] Required course background: 601.220 & 601.226 
MWF 10 
601.633 (600.463) 
INTRO ALGORITHMS (3) Braverman
Same as 601.433, for graduate students. [Analysis] Prereq: 601.226 and 553.171 or Perm. Req'd. Students may receive credit for only one of 601.433/633. 
TuTh 121:15 
601.636 (600.673) (E,Q) 
ALGORITHMIC GAME THEORY Dinitz Same as EN.601.436, for graduate students. Students may receive credit for 601.436 or 601.636, but not both. Prereq: 601.433/633 or permission. [Analysis] Prereq: 600.433/633 or permission. 
TuTh 34:15 
601.641 (600.451) (E) NEW COURSE! 
BLOCKCHAINS AND CRYPTOCURRENCIES Jain [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 
601.648 (600.638) CANCELLED 
COMPUTATIONAL GENOMICS: DATA ANALYSIS (3) Battle Same as 601.438, for graduate students. Students may receive credit for only one of 601.438/638. [Applications] Recommended Course Background: 601.226 or other programming experience, probability and statistics, linear algebra or calculus. 
CANCELLED 
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 (3) VanDurme Same as 601.464, for graduate students. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 
TuTh 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) TENTATIVE 
MACHINE LEARNING (3) staff
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. 
MWF 4:305:45 TENTATIVE

601.676 (600.676) 
MACHINE LEARNING: DATA TO MODELS Saria Same as 601.476, for graduate students. [Analysis or Applications] Students may receive credit for only one of 601.476/676. Prereqs: 1) Intro Prob/Stat, Linear Algebra and Intro Machine Learning OR 2) Strong background in statistics (at least 12 upper level classes in statistics) and programming (fluency with ideally Python and in the very least R/Matlab). 
TuTh 4:305:45 
601.679 (600.479/679) NOT OFFERED 
REPRESENTATION LEARNING (3) Arora
Same as 601.479, for graduate students. [Analysis or Applications]
Required course background: machine learning or basic probability and linear algebra; mathematical maturity. 
?? 
601.682 NEW COURSE! 
MACHINE LEARNING: DEEP LEARNING (3) Hager
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. Undergrads should enroll in EN.601.488 instead. 
TuTh 4:305:45 
601.691 NEW COURSE! 
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 
601.718 NEW COURSE! 
ADVANCED OPERATING SYSTEMS R. Huang Students will study advanced operating system topics and be exposed to recent developments in operating systems research. This course involves readings on classic and new papers. Topics include virtual memory management, synchronization and communication, file systems, protection and security, operating system structure and extension techniques, fault tolerance, and history and experience of systems programming. [Systems] Prereq: 600/601.318/418/618 or permission. 
TuTh 1:302:45 
650.724 (was 650.624) (E) 
ADVANCED NETWORK SECURITY (3) Nielson [Crosslisted from ISI] (see description) [Systems] Required course background: 601.444/644 or permission. 
MW 34:15 
601.745 NEW COURSE! 
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. 
MW 121:15 
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. 
TuTh 1:302:45 
601.765 NEW COURSE! 
MACHINE LEARNING: LINGUISTIC & SEQUENCE MODELING Eisner/Cotterell 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 
601.779 NEW COURSE! 
MACHINE LEARNING: ADVANCED TOPICS Arora This course will focus on recent advances in machine learning. Topics will vary from year to year. The course will be project focused and involve presenting and discussing recent research papers. Prereq: 600/601.475/675 or 600/601.775 or 600/601.479/679 or 600/601.476/676 or equiv. 
Fri 1:304p 
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(600.767) 
SELECTED TOPICS IN SYSTEMS RESEARCH Amir Students will review, present, and discuss current research in computer systems, distributed systems, and computer networks, in the contexts of dependability, performance and scalability. 
Th 34:15 
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 
601.833 (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. 
Th 1:302:30 
601.856 (600.746) 
SEMINAR: MEDICAL IMAGE ANALYSIS Taylor & Prince 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 
601.865 (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. 
Th 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 
601.875 (600.775) NOT OFFERED 
SELECTED TOPICS IN MACHINE LEARNING Saria, 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 stateoftheart across application areas of machine learning and data intensive computing will be read. Student volunteers lead individual meetings. Faculty and external speakers present from timetotime. Required course background: a machine learning course or permission of instructor. 
Thu 34:15 
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 Khudanpur CLSP seminar series, for any students interested in current topics in language and speech processing. 
Tu & Fr 121:15 
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 (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  Andreas Terzis 24  Raman Arora (ugrad research use 517, not 507) 25  Rai Winslow 26  Misha Kazhdan 27  Chris CallisonBurch 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  ChienMing Huang 58  Will Gray (ugrad research use 517, not 507) 59  Kevin Duh 60  Marin Kobilarov