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 posted schedule are noted in red.
600.104 (H) 
COMPUTER ETHICS (1) Mitchell 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. 
We 68p, alternate weeks (start tba) 
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 
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 600.108 concurrently in Fall/Spring semesters. Prereq: familiarity with computers. Students may receive credit for 600.107 or 600.112, but not both. 
MW 1:302:45 
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: 600.107. 
Sec 1: Wed 69p, limit 24 
600.120 (E)

INTERMEDIATE PROGRAMMING (4) More/Kazhdan 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, 600.107, 600.112 or equivalent. 
Sec 01 (More): MWF 121:15, limit 34 
600.226 (E,Q) 
DATA STRUCTURES (4) Froehlich/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: 600.107 or 600.120. 
Sec 01 (Selinski): MWF 1:302:45, limit 75 
600.233 (E) 
COMPUTER SYSTEM FUNDAMENTALS (3) Froehlich [Formerly 600.333/433] 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: 600.120. Students may receive credit for only one of 600.233, 600.333 or 600.433. 
MWF 1:30 
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: 600.120 and 600.226. 
WF 34:15 
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: 550.171. 
TuTh 1:302:45 
600.316 (E) 
DATABASE SYSTEMS (3) Ahmad This course serves as an introduction to the architecture and design of modern database management systems. topics include query processing algorithms and data structures, data organization and storage, query optimization and cost modeling, transaction management and concurrency control, highavailability mechanisms, parallel and distributed databases, and a survey of modern architectures including NoSQL, columnoriented and streaming databases. Course work includes programming assignments and experimentation in a simple database framework written in Java. [Systems] Prereq: 600.120 and 600.226. Students may receive credit for 600.316 or 600.416, but not both. 
MW 121:15 
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: 600.226 and 600.233. Students may receive credit for 600.320 or 600.420, but not both. 
MW 4:305:45 
600.325 (E) 
DECLARATIVE METHODS (3) Eisner Suppose you could simply write down a description of your problem, and let the computer figure out how to solve it. What notation could you use? What strategy should the computer then use? In this survey class, you'll learn to recognize when your problem is an instance of satisfiability, constraint programming, logic programming, dynamic programming, or mathematical programming (e.g., integer linear programming). For each of these related paradigms, you'll learn to reformulate hard problems in the required notation and apply offtheshelf software that can solve any problem in that notation  including NPcomplete problems and many of the problems you'll see in other courses and in the real world. You'll also gain some understanding of the generalpurpose algorithms that power the software. [Analysis] Prereq: 600.226, Calc II. Students can only receive credit for 600.325 or 600.425, not both. 
MWF 3 
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: 600.120, 600.226, 600.233; 600.271 recommended 
MWF 10 
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 
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: 2/13  3/15. 
MW 4:305:45 
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 600.321/421. 
Tue (was Th) 4:307p 
600.416 (E) 
DATABASE SYSTEMS (3) Ahmad Similar material as 600.316, covered in more depth. Intended for upperlevel undergraduates and graduate students. [Systems] Required course background: 600.120 and 600.226. Students may receive credit for 600.316 or 600.416, but not both. 
MW 121:15 
600.420 (E) 
PARALLEL PROGRAMMING (3) Burns Graduate level version of 600.320. Students may receive credit for 600.320 or 600.420, but not both. [Systems]
Required course background: 600.226 and 600.233 or equiv. 
MW 4:305:45 
600.425 (E) 
DECLARATIVE METHODS (3) Eisner Graduate level version of 600.325. [Analysis] Required course background: 600.226, Calc II. Students can only receive credit for 600.325 or 600.425, not both. 
MWF 3 
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: 600.226. Freshmen and sophomores by permission only. 
MW 1:302:45 
600.428 (E) 
COMPILERS & INTERPRETERS (3) Froehlich Graduate level version of 600.328. Students may receive credit for 600.328 or 600.428, but not both. [Systems] Prereq: 600.120 & 600.226 
MWF 10 
600.430 (HQ) 
ONTOLOGIES AND KNOWLEDGE REPRESENTATION (3) Rynasiewicz (Colisted in Philosophy: AS.150.429) Knowledge representation deals with the possible structures by which the content of what is known can be formally represented in such a way that queries can be posed and inferences drawn. Ontology concerns the hierarchical classication of entities from given domains of knowledge together with the relations between various classes, subclasses, or individuals. The main framework in which we will work is that of description logics, which are decidable fragments of varying degrees of first order predicate logic. In ontology development we will examine RDF (Resource Description Framework), its extension to RDFS, and OWL (Web Ontology Language), and use the software Protege' for specific applications. Finally, we will take a look at query languages such as SPARQL (SPARQL Protocol and RDF Query Language). [Analysis] Required course background: 600.107 or equivalent. 
TuTh 34:15 
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: 600.226; Recommended: linear algebra, prob/stat. Students can only receive credit for 600.335 or 600.435, not both. 
TuTh 1:302:45 
600.436 (E) 
ALGORITHMS FOR SENSORBASED ROBOTICS (3) Leonard [Formerly 600.336.] 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: 600.226, linear algebra, probability. Students may receive credit for only one of 600.336, 600.436 and 600.636. 
TuTh 121:15 
600.438 (E) 
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: 600.226 or other programming experience, probability and statistics, linear algebra or calculus. Students may receive credit for 600.338 or 600.638, but not both. 
TuTh 9 
600.444 (E) 
COMPUTER NETWORKS (3) Green 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] Required course background: EN.600.120 and EN.600.233/433 or permission. Students can only receive credit for 600.344 or 600.444, not both. 
MW 121:15 
600.446 (E) 
COMPUTER INTEGRATED SURGERY II (3) Taylor This weekly lecture/seminar course addresses similar material to 600.445, 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 600.445, 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 600.452. Students may also take this course as 600.646. The only difference between 600.446 and 600.646 is the level of project undertaken. Typically, 600.646 projects require a greater degree of mathematical, image processing, or modeling background. Prospective students should consult with the instructor as to which course number is appropriate. [Applications]
Prereq: 600.445/645 or perm req'd. Students may receive credit for
600.446 or 600.646, but not both. 
TuTh 1:302:45 
600.452 (E) 
COMPUTER INTEGRATED SURGERY SEMINAR (1) Taylor Lecture only version of 600.446 (no project). Prereq: 600.445 or perm req'd. Students may receive credit for 600.446 or 600.452, but not both. 
TuTh 1:302:45 
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: 600.226 and 550.171 or Perm. Req'd. Students may receive credit for 600.363 or 600.463, but not both. 
TuTh 121:15 
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: 600.226. 
TuTh 34:15 
600.469 (E,Q) 
APPROXIMATION ALGORITHMS 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. Students may receive credit for 600.469 or 600.669, but not both. [Analysis] Prereq: 600.363/463 or permission. 
TuTh 121:15 (was 34:15) 
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] Required course background: multivariable calculus, probability, linear algebra. 
MWF 34:15

600.476 (EQ) 
MACHINE LEARNING: DATA TO MODELS (3) Saria [Formerly "Machine Learning in Complex Domains"] 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 
600.484 (E) 
AUGMENTED REALITY (3) Navab
Undergraduate level version of 600.684.
[Applications] Students may receive credit for 600.484 or 600.684, but
not both. Prerequisites: EN.600.120, EN.600.226, linear algebra. 
TuTh 910:15 
600.488 
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., 600.226 or equiv. 
TuTh 4:305:45 
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. 
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. 
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. 
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. 
600.520 
SENIOR HONORS THESIS (3) For computer science majors only, a continuation of 600.519. Prerequisite: 600.519 
See below for faculty section numbers. 
600.546 
SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3) Prereq: 600.445 or perm req'd. 
Section 01: Taylor 
600.592 
COMPUTER SCIENCE WORKSHOP II [Previously numbered 492] 
See below for faculty section numbers. 
600.602 
Required for all CS PhD students. Strongly recommended for MSE students. 
TuTh 10:3012 
600.625 
EVENT SEMANTICS IN THEORY AND PRACTICE Van Durme & Rawlins This course explores selected topics in the nature of event representations from the perspective of cognitive science, computer science, linguistics, and philosophy. These fields have developed a rich array of scientific theories about the representation of events, and how humans make inferences about them  we investigate how (and if) such theories could be applied to current research topics and tasks in computational semantics such as inference from text, automated summarization, veridicality assessment, and so on. In addition to classic articles dealing with formal semantic theories, the course considers available machinereadable corpora, ontologies, and related resources that bear on event structure, such as WordNet, PropBank, FrameNet, etc.. The course is aimed to marry theory with practice: students with either a computational or linguistic background are encouraged to participate. [Applications] 
TuTh 1:302:45 
600.636 
ALGORITHMS FOR SENSORBASED ROBOTICS Leonard [Formerly 600.436.] Graduate level version of 600.436 (see description above). [Analysis] Required course background: 600.226, calculus, prob/stat. Students may receive credit for only one of 600.336, 600.436 or 600.636. 
TuTh 121:15 
600.638 
COMPUTATIONAL GENOMICS: DATA ANALYSIS (3) Battle Graduate version of 600.338. Students may receive credit for 600.338 or 600.638, but not both. [Applications] Recommended Course Background: 600.226 or other programming experience, probability and statistics, linear algebra or calculus. Students may receive credit for 600.338 or 600.638, but not both. 
TuTh 9 
600.640 
FRONTIERS OF SEQUENCING DATA ANALYSIS Langmead Public archives now contain petabytes of valuable but hardtoanalyze DNA sequencing data. Analyzing even small datasets is complicated by sequencing errors, differences between individuals, and the fragmentary nature of the the sequencing reads. In this course, we study recent algorithms and methods that seek to make sense of DNA sequencing datasets from small to very large. Topics covered will vary from year to year, but could include RNA sequencing data analysis, other functional genomics data analysis, metagenomics analysis, data compression, indexing, applications of streaming algorithms and sketch data structures, assembly, etc. There will be homework assignments and a course project. [Applications] Prereq: 600.439/639 or permission. 
TuTh 34:15 (was 121:15) 
600.642 
ADVANCED TOPICS IN CRYPTOGRAPHY (3) Jain [Crosslisted in ISI] This course will focus on advanced cryptographic topics with an emphasis on open research problems and student presentations. [Analysis] Prereq: 600.442 or 600.472 or permission. 
F 1:304 
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 or 600.424; or permission of instructor. 
TuTh 910:15 
600.646 
COMPUTER INTEGRATED SURGERY II Taylor Advanced version of 600.446. [Applications] Prereq: 600.445/645 or perm req'd. Students may receive credit for 600.446 or 600.646, but not both. 
TuTh 1:302:45 
600.649 NEW COURSE! 
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 
600.666 ADDED 
INFORMATION EXTRACTION Khudanpur [Colisted as EN.520.666.] Introduction to statistical methods of speech recognition (automatic transcription of speech) and understanding. The course is a natural continuation of 600.465 but is independent of it. topics include elementary information theory, hidden Markov models, the Baum and Viterbi algorithms, efficient hypothesis search methods, statistical decision trees, the estimationmaximization (EM) algorithm, maximum entropy estimation and estimation of discrete probabilities from sparse data for acoustic and language modeling. Weekly assignments and several programming projects. [Applications] Prerequisites: 550.310 and 600.120 or equivalent. Colisted with 050.666 and 520.666. 
MWF 1:30 
600.667 
ADVANCED DISTRIBUTED SYSTEMS AND NETWORKS Amir The course explores the state of the art in distributed systems, networks and Internet research and practice, trying to see what it would take to push the envelop a step further. The course is conducted as a discussion group, where the professor and students brainstorm and pick interesting semesterlong projects with high potential future impact. Example areas include robust scalable infrastructure (distributed datacenters, cloud networking, scada systems), realtime performance (remote surgery, trading systems), hybrid networks (mesh networks, 34G/Wifi/Bluetooth). Students should feel free to bring their own topics of interest and ideas. [Systems] Prereq: a systems course (distributed systems, operating systems, computer networks, parallel programming), or permission of instructor. 
TuTh 34:15 
600.676 
MACHINE LEARNING: DATA TO MODELS Saria [Formerly "Machine Learning in Complex Domains"] Graduate version of 600.476. [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 
600.678 NEW COURSE! 
ADVANCED TOPICS IN CAUSAL INFERENCE (3) Shpitser This course will cover advanced topics on all areas of causal inference, including learning causal effects, pathspecific effects, and optimal policies from data featuring biases induced by missing data, confounders, selection, and measurement error, techniques for generalizing findings to different populations, complex probabilistic models relevant for causal inference applications, learning causal structure from data, and inference under interference and network effects. The course will feature a final project which would involve either an applied data analysis problem (with a causal inference flavor), a literature review, or theoretical work. [Analysis] Prerequisite: EN.600.477/677 or permission. 
MW 34:15 
600.683 NEW COURSE! 
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.600.461/661) and machine learning (EN.600.475) suggested but not required. 
TuTh 910:15 
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 
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., 600.226 or equiv. Undergrads may enroll by permission only. 
TuTh 4:305:45 
600.692 
UNSUPERVISED LEARNING: FROM BIG DATA TO LOWDIMENSIONAL REPRESENTATIONS (was ADVANCED TOPICS IN MACHINE LEARNING: MODELING & SEGMENTATION OF MULTIVARIATE MIXED DATA) Vidal In the era of data deluge, the development of methods for discovering structure in highdimensional data is becoming increasingly important. This course will cover stateoftheart methods from algebraic geometry, sparse and lowrank representations, and statistical learning for modeling and clustering highdimensional data. The first part of the course will cover methods for modeling data with a single lowdimensional subspace, such as PCA, Robust PCA, Kernel PCA, and manifold learning techniques. The second part of the course will cover methods for modeling data with multiple subspaces, such as algebraic, statistical, sparse and lowrank subspace clustering techniques. The third part of the course will cover applications of these methods in image processing, computer vision, and biomedical imaging. [Applications] Required course background: linear algebra, optimization and statistics. Prior exposure to machine learning (e.g., 600.475) is a plus. 
MF 121:15 
520.702 
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING Khudanpur CLSP seminar series, for any students interested in current topics in language and speech processing. 
Tu & Fr 121:15 
600.716 
SELECTED TOPICS ON INNOVATIVE DATA SYSTEMS Ahmad This weekly reading group will survey and dissect the cuttingedge on innovative data systems research. Topics will encompass methods and abstraction in core systems and data management areas (e.g., cloud computing, scalable programming and storage), as well as usecases and "war" stories from industry, and science and engineering applications. Our semester schedule is posted at damsel.cs.jhu.edu/blockparty. 
cancelled 
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 
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 
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 
600.756 NEW COURSE! 
INTRODUCTION TO GEOMETRY PROCESSING Kazhdan In this course we will look at fundamental techniques in geometry processing, including smoothing / sharpening, parameterization, and (if time allows) vector fields. The course will begin with a review of the discretization of the underlying concepts from differential geometry (e.g. normals, curvature, Laplacian) before proceeding to specific applications. Required course background: EN.600.357/457 Computer Graphics. 
Wed 34:45 
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 
600.764 ADDED 
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 
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 
600.766 
SELECTED TOPICS IN MEANING, TRANSLATION AND GENERATION OF TEXT VanDurme A seminar focussed on current research and survey articles on computational semantics. 
Fr 1010:50 
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 
600.775 
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 
600.802 
DISSERTATION RESEARCH 
See below for faculty section numbers. 
600.804 
GRADUATE RESEARCH Independent research for masters or predissertation PhD students. Permission required. 
See below for faculty section numbers. 
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 
600.810 
GRADUATE INDEPENDENT STUDY Permission Required. 
See below for faculty section numbers. 
01  Xin Li 02  Rao Kosaraju 03  Yanif Ahmad 04  Russ Taylor (ugrad research use 518, not 508) 05  Scott Smith 06  Joanne Selinski 07  Harold Lehmann 08  John Sheppard 09  Greg Hager 10  Gregory Chirikjian 11  Sanjeev Khudhanpur 12  Yair Amir 13  David Yarowsky 14  Noah Cowan 15  Randal Burns 16  Jason Eisner (ugrad research use 518, not 508) 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 518, not 508) 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 518, not 508) 36  Emad Boctor (ugrad research use 518, not 508) 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 518, not 508) 50  Joshua Vogelstein 51  Ilya Shpitser 52  Austin Reiter 53  Tamas Budavari 54  Alan Yuille 55  Peter Freeman