Courses without end times are assumed to meet for 50 minute periods. Final room assignments will be available on the Registrar's website in September. Changes to the original schedule are noted in red.
600.104 (H) 
COMPUTER ETHICS (2) Freeman 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. 
Th 4:306:15 
600.107 (E) 
INTRO TO PROGRAMMING IN JAVA (3) Selinski 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 operations, control structures, arrays, functions, recursion, dynamic memory allocation, text files, class usage and class writing. Program design and testing are also covered, in addition to more advanced 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. 
Sec 01: MW 1:302:45, limit 120 
600.108 (E) 
INTRO PROGRAMMING LAB (1) Selinski Satisfactory/Unsatisfactory only. This course is intended for novice programmers and must be taken in conjunction with 600.107. The purpose of this course is to give novice programmers extra handson practice with guided supervision. Students will work in pairs each week to develop working programs, with checkpoints for each development phase. Students may receive credit for 600.108 or 600.113, but not both. Coreq: 600.107. 
Sec 1: Wed 6:009:00p, limit 24 
600.120 (E)

INTERMEDIATE PROGRAMMING (4) More/Mitchell 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, 600.111, 600.112 or equivalent. 
Sec 01 (More): MWF 12:001:15 CS/CE majors/minors only 
600.226 (E,Q) 
DATA STRUCTURES (4) Froehlich/Schatz This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prereq: AP CS or 600.107 or 600.120 or equivalent. 
Sec 01 (Froehlich): MWF 121:15, limit 75 
600.233 (E) 
COMPUTER SYSTEM FUNDAMENTALS (3) Koehn [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 10 
600.255 (E) 
INTRODUCTION TO VIDEO GAME DESIGN (3) Froehlich A broad survey course in video game design (as opposed to mathematical game theory), covering artistic, technical, as well as sociological aspects of video games. Students will learn about the history of video games, archetypal game styles, computer graphics and programming, user interface and interaction design, graphical design, spatial and object design, character animation, basic game physics, plot and character development, as well as psychological and sociological impact of games. Students will design and implement an experimental video game in interdisciplinary teams of 34 students as part of a semesterlong project. Prereq: sophomores and above, permission of instructor; Coreq: 600.256. Section 1 requires technical skills, including at least one programming course (preferably 2 or more). Section 2 requires artistic skills, including at least one multimedia course (preferably 2 or more). 
MW 4:305:45 
600.256 (E) 
INTRODUCTION TO VIDEO GAME DESIGN LAB (1) Froehlich A lab course in support of 600.255: Introduction to Video Game Design covering a variety of multimedia techniques and applications from image processing, through sound design, to 3D modeling and animation. See 600.255: Introduction to Video Game Design for details about enrolling. Unlike in 600.255, the sections for the lab are meant to have a crosssection of students from different backgrounds. Ideally students working on a team project will be enrolled in the same lab section. Coreq: 600.255. 
Sec 1: M 69 
600.271 (E,Q) 
AUTOMATA and COMPUTATION THEORY (3) More This course is an introduction to the theory of computing. topics include design of finite state automata, pushdown automata, linear bounded automata, Turing machines and phrase structure grammars; correspondence between automata and grammars; computable functions, decidable and undecidable problems, P and NP problems, NPcompleteness, and randomization. Students may not receive credit for 600.271 and 600.471 for the same degree. Prereq: 550.171. 
TuTh 1:302:45 
600.315 (E) 
DATABASES (3) Yarowsky Introduction to database management systems and database design, focusing on the relational and objectoriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 600.226. Students may receive credit for 600.315 or 600.415, but not both. 
TuTh 34:15 
600.318 (E) 
OPERATING SYSTEMS (3) Froehlich This course covers the fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multiprogramming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system. [Systems] Prereq: 600.120, 600.226, and 600.233. 600.211 Recommended. Students may receive credit for 600.318 or 600.418, but not both. 
MWF 10 
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: 600.120 and 600.226; 600.233 recommended. Students may receive credit for 600.320 or 600.420, but not both. 
TuTh 4:305:45 
600.321 (E) 
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Smith This course covers objectoriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include objectoriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. [Systems or Applications] (http://pl.cs.jhu.edu/oose/index.shtml) Prereq: 600.226 and 600.120. Students may receive credit for 600.321 or 600.421, but not both. 
MW 1:302:45 
600.337 (E) 
The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive handson experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester and is a good introduction course to the 600.667 Advanced Distributed Systems and Networks projectfocused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses/cs437) Prereq: 600.120, 600.226. Students may receive credit for 600.337 or 600.437, but not both. 
TuTh 34:15 
600.365 (E) NEW COURSE! 
KNOWLEDGE DISCOVERY FROM TEXT (3) VanDurme & Lippincott The world is full of text: webpages, emails, newspaper articles, tweets, medical records, and so on. The purpose of text is for people to convey knowledge to other people. This course focuses on how computers analyze large, potentially streaming, text collections to automatically discover knowledge on their own (and to help people better find it themselves). Lectures and assignments will cover relevant topics in automatic classification (applied machine learning), linguistics, highperformance computing, and systems engineering, working with software systems for automatic question answering, populating knowledge bases, and aggregate analysis of social media such as Twitter. [Applications] Prereqs: 600.120 & 600.226. 
TuTh 34:15 
600.415 (E) 
DATABASES (3) Yarowsky Graduate level version of 600.315. Students may receive credit for 600.315 or 600.415, but not both. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 600.226. 
TuTh 34:15 
600.418 (E)

OPERATING SYSTEMS (3) Froehlich Similar material as 600.318, covered in more depth. Intended for upperlevel undergraduates and graduate students. [Systems] Required course background: 600.226 and 600.233; 600.211 recommended. Students may receive credit for 600.318 or 600.418, but not both. 
MWF 10 
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: 600.120 or equiv. 
TuTh 4:305:45 
600.421 (E) 
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Smith Graduate level version of 600.321. Students may receive credit for 600.321 or 600.421, but not both. [Systems or Applications] (http://pl.cs.jhu.edu/oose/index.shtml) Prereq: 600.226 and 600.120. 
MW 1:302:45 
600.424 (E) 
NETWORK SECURITY (3) Nielson [Crosslisted in ISI] This course focuses on communication security in computer systems and networks. The course is intended to provide students with an introduction to the field of network security. The course covers network security services such as authentication and access control, integrity and confidentiality of data, firewalls and related technologies, Web security and privacy. Course work involves implementing various security techniques. A course project is required. [Systems] Required course background: 600.120, 600.226, 600.344/444 or permission. 
MW 34:15 
600.437 (E) 
Graduate version of 600.337. This course is taught every other Fall semester and is a good introduction course to the 600.667 Advanced Distributed Systems and Networks projectfocused course that is offered in the following Spring with an eye toward entrepreneurship. Prereq: intermediate programming in C/C++ and data structures. Students may receive credit for 600.337 or 600.437, but not both. [Systems] (www.cnds.jhu.edu/courses/cs437) Prereq: 600.120, 600.226. 
TuTh 34:15 
600.439 (E) 
COMPUTATIONAL GENOMICS (3) Langmead Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, realworld genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming projects. [Applications] Prereq: 600.120 & 600.226. 
TuTh 121:15 
600.442 (E,Q) 
MODERN CRYPTOGRAPHY (3) Jain Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zeroknowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. [Analysis] Prerequisite: 600.363/463, 600.271/471 and 550.171 or equiv. 
MW 1:302:45 
600.443 (E)

SECURITY AND PRIVACY IN COMPUTING (3) Rubin Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semesterlong project that will be done in teams and will include a presentation by each group to the class. [Applications] Prerequisite: A basic course in operating systems and networking, or permission of instructor. 
TuTh 910:15 
600.445 (E) 
COMPUTER INTEGRATED SURGERY I (4) Taylor This course focuses on computerbased techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and followup. It emphasizes the relationship between problem definition, computerbased technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. [Applications] (Graduate students should enroll with course number 600.645.) Students may earn credit for 600.445 or 600.645, but not both. (http://www.cisst.org/~cista/445/index.html) Prereq: 600.226 and linear algebra, or permission. Recmd: 600.120, 600.457, 600.461, image processing. 
TuTh 1:302:45 
600.454 (E)

PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green [Colisted with 650.445.] This semesterlong course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widelydeployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem. We will place a particular emphasis on the techniques of provable security and the feasibility of reverseengineering undocumented cryptographic systems. [Systems] 
MW 121:15 
600.457 (E,Q)

COMPUTER GRAPHICS (3) Kazhdan This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. Students may receive credit for 600.357 or 600.457, but not both. [Applications] Prereq: no audits; 600.120 (C++), 600.226, linear algebra. Permission of instructor is required for students not satisfying a prerequisite. Students may receive credit for 600.357 or 600.457, but not both.

MWF 11 
600.461 (E,Q) 
COMPUTER VISION (3) Reiter [Formerly 600.361] This course gives an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3D geometry from binocular stereo, motion, and photometric stereo; and object recognition. Edge detection and color perception are covered as well. Elements of machine vision and biological vision are also included. Students can earn credit for at most one of 600.361/461/661. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Prereq: intro programming, linear algebra, prob/stat. 
TuTh 121:15 
600.463 (E,Q) 
INTRO ALGORITHMS (3) Dinitz This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as 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 1:302:45 
600.464 (E,Q) 
RANDOMIZED & BIG DATA ALGORITHMS (3) Braverman The course emphasizes algorithmic design aspects, and how randomization can be a helpful tool. The topics covered includee: tail inequalities, linear programming relaxation & randomized rounding, derandomization, existence proofs, universal hashing, markov chains, metropolis and metropolishastings methods, mixing by coupling and by eigenvalues, counting problems, semidefinite programming and rounding, lower bound arguments, and applications of expanders. [Analysis] (www.cs.jhu.edu/~cs464) Prereq: 600.363/463 and Probability. Students may receive credit for 600.464 or 600.664, but not both. 
TuTh 121:15

600.465 (E) 
NATURAL LANGUAGE PROCESSING (4) Eisner This course is an indepth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers? The course covers methods for trees (parsing and semantic interpretation), sequences (finitestate transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, partofspeech tagging, speech recognition and machine translation. There are a number of structured but challenging programming assignments. [Applications] (www.cs.jhu.edu/~jason/465) Prerequisite: 600.226. 
MWF 34:15, section T 67:30p 
600.471 (EQ) 
THEORY OF COMPUTATION (3) Li This is a graduatelevel course studying the theoretical foundations of computer science. Topics covered will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 600.271 and 600.471, unless one is for an undergrad degree and the other for grad. [Analysis] Prereq: discrete math or permission. 
TuTh 121:15 
600.475 (E) 
INTRODUCTION TO MACHINE LEARNING (3) Dredze
This course takes an application driven approach to current topics in machine
learning. The course covers supervised learning (classification/structured
prediction/regression/ranking), unsupervised learning (dimensionality
reduction, bayesian modeling, clustering) and semisupervised learning.
Additional topics may include reinforcement learning and learning theory. The
course will also consider challenges resulting from learning applications,
such as transfer learning, multitask learning and large datasets. We will
cover popular algorithms (naive Bayes, SVM, perceptron, HMM, winnow, LDA,
kmeans, maximum entropy) and will focus on how statistical learning
algorithms are applied to real world applications. Students in the course will
implement several learning algorithms and develop a learning system for a
final project. [Applications] Required course background: multivariate calculus. 
MW 1:30 
600.477 (E,Q) NEW COURSE! 
CAUSAL INFERENCE (3) Shpitser "Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. [Analysis] Prerequisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Students may receive credit for 600.477 or 600.677, but not both. 
TuTh 1:302:45 
600.479 (E) 
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] Students may receive credit for 600.479 or
600.679 but not both.
Required course background: machine learning or basic probability and linear algebra; mathematical maturity. 
TuTh 3:004:15 
600.485 (Q) NEW COURSE! 
PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille [Colisted with AS.050.375 and AS.050.675.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low, mid, and highlevel vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. [Applications or Analysis] Prerequisites: Calc I, programming experience (Python preferred). 
TuTh 910:15 
600.503 
INDEPENDENT STUDY Individual, guided study for undergraduate students under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. 
See below for faculty section numbers 
600.507 
UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. 
See below for faculty section numbers and whether to select 507 or 517. 
600.509 
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study and credit assigned must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit. S/U only. Permission required. 
See below for faculty section numbers 
600.517 
GROUP UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. 
Only for faculty specifically marked below. 
600.519 
SENIOR HONORS THESIS (3) For computer science majors only. The student will undertake a substantial independent research project under the supervision of a faculty member, potentially leading to the notation "Departmental Honors with Thesis" on the final transcript. Students are expected to enroll in both semesters of this course during their senior year. Project proposals must be submitted and accepted in the preceding spring semester (junior year) before registration. Students will present their work publically before April 1st of senior year. They will also submit a first draft of their project report (thesis document) at that time. Faculty will meet to decide if the thesis will be accepted for honors. Prereq: 3.5 GPA in Computer Science after spring of junior year and permission of faculty supervisor. 
See below for faculty section numbers 
600.546 
SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3) The student will undertake a substantial independent research project in the area of computerintegrated surgery, under joint supervision of a WSE faculty adviser and a clinician or clinical researcher at the Johns Hopkins Medical School. Prereq: 600.445 or perm req'd. 
Section 1: Taylor 
600.591 
COMPUTER SCIENCE WORKSHOP I [Formerly 600.491] An 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. Perm. of faculty supervisor req'd. 
See below for faculty section numbers 
600.601 
Required for all fulltime PhD students. Recommended for MSE students. 
TuTh 10:3012 
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 
600.615 
BIG DATA, SMALL LANGUAGES, SCALABLE SYSTEMS Ahmad This class will study domainspecific data management tools, focusing on extremely scalable system design based on the domain's semantic and structural properties. We will study a variety of data models including stream, graph, array and probabilistic data, and their processing on modern architectures such as column and keyvalue stores, stream and XQuery engines. Further topics include the use of novel hardware such as solid state disks, phase change memory, GPUs, and FPGAs. The class includes a semester long group project to develop a query processor for an application of the group's choice (e.g. on system log, finance, web, sensor, speech data). [Systems] (www.cs.jhu.edu/~yanif/teaching/bdslss) Prereq: 600.315/415 or equivalent. 
MW 121:15 
600.629 NEW COURSE! 
WIRELESS NETWORKS (3) Zadeh This course covers the basics of mobile communication and wireless networking for computer science majors by keeping a balance between communication and networking topics. In this course, the students will be exposed to wireless transmission fundamentals (path loss, shadowing, modulation, coding and channel models), and learn about medium access control protocols, wireless local area networks (IEEE 802.11), and wireless mobile networks and applications, including cellular networks (cellular concept, channel reuse, capacity limits, and cellular systems), fourth generation systems, long term evolution (LTE), mobile applications, and mobile IP. [Systems] Prerequisites: 600.344/444 recommended. 
MW 1:30 
600.639 
COMPUTATIONAL GENOMICS Langmead Graduate version of 600.439. Students may earn credit for 600.439 or 600.639, but not both. [Applications] Prereq: 600.120 & 600.226. 
TuTh 121:15 
600.641 
ADVANCED TOPICS IN GENOMIC DATA ANALYSIS Battle [Formerly titled Machine Learning for Genomic Data  Trends and
Applications] Recommended Course Background: coursework in data mining, machine learning. Students may receive credit for 600.441 or 600.641, but not both. 
TuTh 910:15 
600.645 (E) 
COMPUTER INTEGRATED SURGERY I Taylor Graduate version of 600.445 (see description). Students may earn credit for 600.445 or 600.645, but not both. [Applications] (http://www.cisst.org/~cista/445/index.html) Prereq: data structures and linear algebra, or permission. Recommended: intermediate programming in C/C++, 600.457, 600.461, image processing. 
TuTh 1:302:45 
600.661 
COMPUTER VISION (3) Reiter [Formerly 600.461.] Graduate version of 600.461. Students may receive credit for at most one of 600.361/461/661. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Prereq: intro programming, linear algebra, prob/stat 
TuTh 121:15 
600.677 NEW COURSE! 
CAUSAL INFERENCE (3) Shpitser Advanced graduate version of 600.477. [Analysis] Prerequisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Students may receive credit for 600.477 or 600.677, but not both. 
TuTh 1:30 
600.726 
SELECTED TOPICS IN PROGRAMMING LANGUAGES Smith This course covers recent developments in the foundations of programming language design and implementation. topics covered vary from year to year. Students will present papers orally. 
Th 12 
600.728 
SELECTED TOPICS IN CATEGORY THEORY Filardo Students in this course will read a sampling of standard texts in Category Theory (e.g. the books by Awodey, Mac Lane, Pierce, or others) and papers relevant to the research of participants. 
tbd 
500.745 
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, 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 
600.757  SELECTED TOPICS IN COMPUTER GRAPHICS Kazhdan In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week. 
tbd 
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 CANCELLED 
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. 
cancelled

600.765 
SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Eisner A reading group exploring important current research in the field and potentially relevant material from related fields. Enrolled students are expected to present papers and lead discussion. 
Th 12 
600.766 
SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme & Rawlins 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 1011 
AS.050.814 NEW COURSE! 
RESEARCH SEMINAR IN COMPUTER VISION Yuille This course covers advanced topics in computational vision. It discusses and reviews recent progress and technical advances in visual topics such as object recognition, scene understanding, and image parsing. 
tba 
600.801 
PHD RESEARCH Independent research for PhD students. 
See below for faculty section
numbers. 
600.803 
MASTERS RESEARCH Independent research for masters 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 
600.809 
INDEPENDENT STUDY (graduate students) Permission required. 
See below for faculty
section numbers. 
01  Xin Li 02  Rao Kosaraju 03  Yanif Ahmad 04  Russ Taylor (ugrad research use 517, not 507) 05  Scott Smith 06  Joanne Selinski 07  Harold Lehmann 08  John Sheppard 09  Greg Hager 10  Gregory Chirikjian 11  Sanjeev Khudhanpur 12  Yair Amir 13  David Yarowsky 14  Noah Cowan 15  Randal Burns 16  Jason Eisner (ugrad research use 517, not 507) 17  Mark Dredze 18  Michael Dinitz 19  Rachel Karchin 20  Michael Schatz 21  Avi Rubin 22  Matt Green 23  Andreas Terzis 24  Raman Arora (ugrad research use 517, not 507) 25  Rai Winslow 26  Misha Kazhdan 27  Chris 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  Peter Freeman