601.104 
COMPUTER ETHICS (1) Sheela Kosaraju Students will examine a variety of topics regarding policy, legal, and moral issues related to the computer science profession itself and to the proliferation of computers in all aspects of society, especially in the era of the Internet. The course will cover various general issues related to ethical frameworks and apply those frameworks more specifically to the use of computers and the Internet. The topics will include privacy issues, computer crime, intellectual property law  specifically copyright and patent issues, globalization, and ethical responsibilities for computer science professionals. Work in the course will consist of weekly assignments on one or more of the readings and a final paper on a topic chosen by the student and approved by the instructor. 
Sec 01: Mon 4:306:30p, alternate weeks 
601.107 
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 601.108 concurrently in Fall/Spring semesters. Prereq: familiarity with computers. Students may receive credit for 601.107 or 600.112, but not both. 
Sec 01: MW 1:302:45, limit 120 
601.108 
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 601.108 or 600.113, but not both. Coreq: 601.107. 
Sec 1: Wed 6:009:00p, limit 24 
601.220

INTERMEDIATE PROGRAMMING (4) More/Amir This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover 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, 580.200 or equivalent. 
CS/CE majors/minors only 
601.226 
DATA STRUCTURES (4) Froehlich/? This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prereq: AP CS or 600.107 or 600.120 or equivalent. 
Sec 01 (??): MWF 121:15, limit 75 
601.229 
COMPUTER SYSTEM FUNDAMENTALS (3) Froehlich 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 601.229, 600.233, 600.333 or 600.433. 
MWF 10 
601.231 
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 601.231/600.271 and 601.631/600.471 for the same degree. Prereq: 550.171. 
TuTh 910:15 
601.255 
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. [Oral] Prereq: 601.220 and 601.226 or permission of instructor; Coreq: 601.256. Section 1 requires technical skills; section 2 requires artistic skills, including at least one multimedia course (preferably 2 or more). 
MW 4:305:45 
601.256 
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: 601.255. 
Sec 1: M 69 
601.315 
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 only one of 601.315/415/615. 
TuTh 34:15 
601.318 
OPERATING SYSTEMS (3) Huang This course covers the fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, 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. Students may receive credit for only one of 601.318/418/618. 
TuTh 910:15 
601.365

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 
601.415 
DATABASES (3) Yarowsky Similar material as 601.315, covered in more depth, for advanced undergraduates. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 600.226. Students may receive credit for only one of 601.315/415/615. 
TuTh 34:15 
601.418

OPERATING SYSTEMS (3) Huang Similar material as 600.318, covered in more depth, for advanced undergraduates. [Systems] Prereq: 600.120, 600.226 and 600.233. Students may receive credit for only one of 601.318/418/618. 
TuTh 910:15 
601.421 
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), Oral] (http://pl.cs.jhu.edu/oose/index.shtml) Prereq: 600.226 and 600.120. Students may receive credit for only one of 601.421/621. 
MW 1:302:45 
601.433 
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 only one of 601.433/633. 
TuTh 1:302:45 
601.434 
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 550.310 or 550.420/620 or equivalent. Students may receive credit for only one of 601.434/634. 
TuTh 121:15

601.442 
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: 600.271/471 and 550.310/550.420. Students may receive credit for only one of 601.442/642. 
MW 1:302:45 
601.443

SECURITY AND PRIVACY IN COMPUTING (3) Akinyele & Wilson Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semesterlong project that will be done in teams and will include a presentation by each group to the class. [Applications] Prerequisite: 600.233 and (600.318/418 or 600.444) Students may receive credit for only one of 601.443/643. 
MF 4:305:45 
601.444 
NETWORK SECURITY (3) Nielson This course focuses on communication security in computer systems and networks. The course is intended to provide students with an introduction to the field of network security. The course covers network security services such as authentication and access control, integrity and confidentiality of data, firewalls and related technologies, Web security and privacy. Course work involves implementing various security techniques. A course project is required. [Systems] Prerequisites: 600.120, 600.226, 600.344/444 or permission. Students can receive credit for only one of 601.444/644. 
MW 34:15 
601.445

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] Prereq: EN.600.226 and EN.600.233. Students may receive credit for only one of 601.445/645. 
MW 121:15 
601.447 
COMPUTATIONAL GENOMICS: SEQUENCES (3) Langmead Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, 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, Oral] Prereq: 600.120 & 600.226. Students may receive credit for at most one of 601.447/647/747. 
TuTh 121:15 
601.452 
COMPUTATIONAL BIOMEDICAL RESEARCH (3) Schatz
[Colisted with AS.020.415] This course for advanced undergraduates includes classroom instruction in interdisciplinary research approaches and lab work on an independent research project in the lab of a Bloomberg Distinguished Professor and other distinguished faculty. Lectures will focus on crosscutting techniques such as data visualization, statistical inference, and scientific computing. In addition to two 50minute classes per week, students will commit to working approximately 3 hours per week in the lab of one of the professors. The student and professor will work together to schedule the research project. Students will present their work at a symposium at the end of the semester. Prereq: permission required. 
MW 33:50 
601.455 
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] (http://www.cisst.org/~cista/445/index.html) Prereq: 600.226 and linear algebra, or permission. Recmd: 600.120, 600.457, 600.461, image processing. Students may earn credit for only one of 601.455/655. 
TuTh 1:302:45 
601.457

COMPUTER GRAPHICS (3) Kazhdan This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. [Applications] Prereq: no audits; 600.120 (C++), 600.226, linear algebra. Permission of instructor is required for students not satisfying a prerequisite. Students may receive credit for only one of 601.457/657.

MWF 11 
601.461 
COMPUTER VISION (3) Reiter This course gives an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 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. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Prereq: intro programming, linear algebra, prob/stat. Students can earn credit for at most one of 601.461/661/761. 
TuTh 121:15 
601.463 ADDED! 
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: 600.226, linear algebra, probability. Students may receive credit for only one of 600.336/436/636 and 601.463/663/763. 
TuTh 4:305:45 
601.465 
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. Students may receive credit for at most one of 601.465/665. 
MWF 34:15, section T 67:30p 
601.468 
MACHINE TRANSLATION (3) Koehn Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. [Applications] Required course background: prob/stat, 600.226. Student may receive credit for at most one of 601.468/668. 
TuTh 1:302:45 
601.475 
MACHINE LEARNING (3) Dredze
Machine learning is subfield of computer science and artificial
intelligence, whose goal is to develop computational systems,
methods, and algorithms that can learn from data to improve their
performance. This course introduces the foundational concepts of
modern Machine Learning, including core principles, popular
algorithms and modeling platforms. This will include both supervised
learning, which includes popular algorithms like SVMs, logistic
regression, boosting and deep learning, as well as unsupervised
learning frameworks, which include Expectation Maximization and
graphical models. Homework assignments include a heavy programming
components, requiring students to implement several machine learning
algorithms in a common learning framework. Additionally, analytical
homework questions will explore various machine learning concepts,
building on the 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] Prereqs: multivariable calculus (AS.110.202), probability (EN.550.310/EN.550.420), linear algebra (AS.110.201/AS.110.212). Students may receive credit for only one of 601.475/675. 
MW 1:30 
601.477 ADDED! 
CAUSAL INFERENCE (3) Shpitser "Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. [Analysis] Prerequisites: 600.475/675 or stats/probability or permission. Students may receive credit for at most one of 601.477/677. 
TuTh 34:15 
601.485 
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 
601.501 
COMPUTER SCIENCE WORKSHOP [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 
601.503 
INDEPENDENT STUDY Individual, guided study for undergraduate students under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. 
See below for faculty section numbers 
601.507 
UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. 
See below for faculty section numbers and whether to select 507 or 517. 
601.509 
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study and credit assigned must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit. S/U only. Permission required. 
See below for faculty section numbers 
601.517 
GROUP UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. 
Only for faculty specifically marked below. 
601.519 
SENIOR HONORS THESIS (3) For computer science majors only. The student will undertake a substantial independent research project under the supervision of a faculty member, potentially leading to the notation "Departmental Honors with Thesis" on the final transcript. Students are expected to enroll in both semesters of this course during their senior year. Project proposals must be submitted and accepted in the preceding spring semester (junior year) before registration. Students will present their work publically before April 1st of senior year. They will also submit a first draft of their project report (thesis document) at that time. Faculty will meet to decide if the thesis will be accepted for honors. Prereq: 3.5 GPA in Computer Science after spring of junior year and permission of faculty supervisor. 
See below for faculty section numbers 
601.556 
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 
601.615 
DATABASES (3) Yarowsky Same material as 601.415, for graduate students. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html) Required course background: 600.226. Students may receive credit for only one of 601.315/415/615. 
TuTh 34:15 
601.618

OPERATING SYSTEMS (3) Huang Same material as 601.418, for graduate students. [Systems] Required course background: 600.226 and 600.233. Students may receive credit for only one of 601.318/418/618. 
TuTh 910:15 
601.621 
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Smith Same material as 601.421, for graduate students. [Systems or Applications] (http://pl.cs.jhu.edu/oose/index.shtml) Required course background: 600.226 and 600.120. Students may receive credit for only one of 601.421/621. 
MW 1:302:45 
601.633 
INTRO ALGORITHMS Dinitz Same material as 600.433, for graduate students. [Analysis] Prereq: 600.226 and 550.171 or Perm. Req'd. Students may receive credit for only one of 601.433/633. 
TuTh 1:302:45 
601.634 
RANDOMIZED & BIG DATA ALGORITHMS Braverman Same material as 601.434, for graduate students. [Analysis] (www.cs.jhu.edu/~cs464) Prereq: 600.363/463 and probability. Students may receive credit for only one of 601.434/634. 
TuTh 121:15

601.642 
MODERN CRYPTOGRAPHYJain Same material as 601.442, for graduate students. [Analysis] Required course background: Probability and 600.271/471 or equiv. 
MW 1:302:45 
601.643

SECURITY AND PRIVACY IN COMPUTING Akinyele & Wilson Same material as 601.443, for graduate students. [Applications] Required course background: A basic course in operating systems and networking, or permission of instructor. 
MF 4:305:45 
601.644 
NETWORK SECURITY (3) Nielson [Crosslisted in ISI] Same material as 601.444, for graduate students. [Systems] Required course background: 600.120, 600.226, Computer Networks or permission. Students may receive credit for only one of 601.444/644. 
MW 34:15 
601.645

PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green [Colisted with 650.445.] Same material as 601.445, for graduate students. [Systems] Prereqs? Students may receive credit for only one of 601.445/645. 
MW 121:15 
601.647 
COMPUTATIONAL GENOMICS: SEQUENCES Langmead Same material as 601.447, for graduate students. [Applications] Required Course Background: 600.120 & 600.226. Students may earn credit for at most one of 601.447/647/747. 
TuTh 121:15 
601.655 
COMPUTER INTEGRATED SURGERY I Taylor Same material as 601.455, for graduate students. [Applications] (http://www.cisst.org/~cista/445/index.html) Prereq: data structures and linear algebra, or permission. Recommended: intermediate programming in C/C++, computer graphics, computer vision, image processing. Students may earn credit for 601.455 or 601.655, but not both. 
TuTh 1:302:45 
601.657 
COMPUTER GRAPHICS (3) Kazhdan Same material as 601.457, for graduate students. Prereq: no audits; 600.120 (C++), 600.226, linear algebra. Permission of instructor is required for students not satisfying a prerequisite. Students may receive credit for only one of 601.457/657.

MWF 11 
601.661 
COMPUTER VISION Reiter Same material as 601.461, for graduate students. Students may receive credit for at most one of 601.461/661/761. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Required course background: intro programming, linear algebra, prob/stat 
TuTh 121:15 
601.663 ADDED! 
ALGORITHMS FOR SENSORBASED ROBOTICS Leonard Same material as EN.601.463, for graduate students. [Analysis] Required course background: 600.226, calculus, prob/stat. Students may receive credit for only one of 600.336/436/636 or 601.463/663/763. 
TuTh 4:305:45 
601.665 
NATURAL LANGUAGE PROCESSING Eisner Same material as 601.465, for graduate students. [Applications] (www.cs.jhu.edu/~jason/465) Prerequisite: 600.226. Students may receive credit for at most one of 601.465/665. 
MWF 34:15, section T 67:30p 
601.668 
MACHINE TRANSLATION Koehn Same material as 601.468, for graduate students. [Applications] Required course background: prob/stat, 600.226. Student may receive credit for at most one of 601.468/668. 
TuTh 1:302:45 
601.675 
MACHINE LEARNING Dredze
Same material as 601.475, for graduate students.
[Applications or Analysis] Required course background: multivariable calculus, probability, linear algebra. Student may receive credit for only one of 601.475/675. 
MW 1:30 
601.677 ADDED! 
CAUSAL INFERENCE (3) Shpitser Same material as 601.477, for graduate students. [Analysis] Prerequisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Students may receive credit for at most one of 601.477/677. 
TuTh 34:15 
601.685 ADDED! 
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 
601.714 
ADVANCED COMPUTER NETWORKS Jin
This is a graduatelevel course on computer networks. It provides
a comprehensive overview on advanced topics in network protocols
and networked systems. The course will cover both classic papers
on Internet protocols and recent research results. It will examine
a wide range of topics, e.g., routing, congestion control, network
architectures, datacenter networks, network virtualization,
softwaredefined networking, and programmable networks, with an
emphasize on core networking concepts and principles. The course
will include lectures, paper discussions, programming assignments
and a research project. [Systems] Prereq: EN.601.414/614 or equivalent. 
TuTh 1:302:45 
601.723 
ADVANCED TOPICS IN DATAINTENSIVE COMPUTING Burns The advent of cloud computing has lead to an explosion of storage system and data analysis software, including NoSQL databases, bulksynchronous processing, graph computing engines, and stream processing. This course will explore scaleout software architectures for dataprocessing tasks. It will examine the algorithms and datastructures that underlie scalable systems and look at how hardware and networking trends influence the design and deployment of cloud computing. Recommended Course Background: EN.600.320/420 or permission of instructor. [Systems] Prereq: 600.320/420 or equivalent. 
Tu 4:306, Th 4:308 
601.730 
PSEUDORANDOMNESS AND COMBINATORIAL CONSTRUCTIONS Li Randomness is very useful in almost all areas of computer science, such as algorithms, distributed computing and cryptography. However, computers generally do not have access to truly uniform random bits. To deal with this, we rely on various pseudorandom objects to reduce either the quantity or the quality of the random bits needed. In this course, we will develop provably good pseudorandom objects for a variety of tasks. We will frequently require explicit combinatorial constructions. That is, we will want to efficiently and deterministically construct such objects. Along the way, we will also explore the close connections of such objects to many other areas in computer science and mathematics, such as graph theory, coding theory, complexity theory and arithmetic combinatorics. [Analysis] Required Course Background: 600.271/471, and probability. 
TuTh 1:302:45 
601.747 
COMPUTATIONAL GENOMICS: SEQUENCES Langmead Similar material as 601.447/647, covered in more depth. [Applications] Required Course Background: 600.120 & 600.226. Students may earn credit for at most one of 601.447/647/747. 
Cancelled  merged with 601.647. 
601.751 
ADVANCED TOPICS IN GENOMIC DATA ANALYSIS Battle [Formerly titled Machine Learning for Genomic Data  Trends and
Applications] Recommended Course Background: coursework in data science or machine learning. 
MW 910:15 
601.761 
COMPUTER VISION Reiter Similar material as 601.461/661, covered in more depth. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Required course background: intro programming, linear algebra, prob/stat. Students may receive credit for at most one of 601.461/661/761. 
Cancelled  merged with 601.661. 
601.763 
ALGORITHMS FOR SENSORBASED ROBOTICS Leonard Similar material as 601.463/663, covered in more depth. [Analysis] Required course background: 600.226, calculus, prob/stat. Students may receive credit for only one of 600.336/436/636 or 601.463/663/763. 
Cancelled  merged with 601.663. 
EN.601.775 ADDED! 
STATISTICAL MACHINE LEARNING Arora This is a second graduate level course in machine learning. It will provide a formal and an indepth coverage of topics at the interface of statistical theory and computational sciences. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall computational runtime (computation or iteration complexity). We will cover topics including nonparametric methods, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in largescale machinelearning and randomized projections. Topics will vary from yeartoyear but the general focus would be on combining methodology with theoretical and computational foundations. [Analysis or Applications] Prereq: 600.475/601.475/601.675 or equivalent or permission. 
MW 34:15 
601.779

ADVANCED TOPICS IN REPRESENTATION LEARNING (3) Arora This course will focus on recent advances in theory, methods and applications of representation learning. We will take a stochastic optimization view of representation learning, and explore various learning objectives and optimization algorithms for representation learning. In this stochastic optimization framework, we will present a unified view of different approaches to representation learning including function approximation, probabilistic, and information theoretic. We will study the optimization problems that result in each of these approaches and study algorithms for solving these optimization problems, both theoretically and empirically. Finally, we will discuss applications of these techniques to speech and language processing, computational healthcare, social media analytics, and computational neuroscience. [Analysis] Prerequisites: EN.600.479/679 Representation Learning or permission (requiring all of the following):

MW 34:15 
601.801 
Required for all fulltime PhD students. Recommended for MSE students. 
TuTh 10:3012 
601.803 
MASTERS RESEARCH Independent research for masters students. Permission required. 
See below for faculty section
numbers. 
601.805 
GRADUATE INDEPENDENT STUDY Permission required. 
See below for faculty
section numbers. 
601.807 
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 
PHD RESEARCH Independent research for PhD students. 
See below for faculty section
numbers. 
AS.050.814 
RESEARCH SEMINAR IN COMPUTER VISION Yuille This course covers advanced topics in computational vision. It discusses and reviews recent progress and technical advances in visual topics such as object recognition, scene understanding, and image parsing. 
tba 
601.826 
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 
601.831 
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 
SEMINAR IN ALGORITHMS Braverman This course will explore algorithms and theoretical computer science with a focus on algorithms for massive data. Examples of topics include streaming algorithms, approximation algorithms, online algorithms. Students will be encouraged to select a paper and lead a discussion. External speakers will be invited to present current work as well. This course is a good opportunity for motivated students to learn modern algorithmic methods. Prereq: 600.463 or equivalent. 
offered?

601.857  SELECTED TOPICS IN COMPUTER GRAPHICS Kazhdan In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week. 
tbd 
601.865 
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 
601.866 
SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme A seminar focussed on current research and survey articles on computational semantics. 
Fr 10:4511:45 
601.868 
SELECTED TOPICS IN MACHINE TRANSLATION Koehn Students in this course will review, present, and discuss current research in machine translation. Prereq: permission of instructor. 
W 11noon 
601.875 
SELECTED TOPICS IN MACHINE LEARNING Arora This seminar is recommended for all students interested in data intensive computing research areas (e.g., machine learning, computer vision, natural language processing, speech, computational social science). The meeting format is participatory. Papers that discuss best practices and the 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 
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 
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 
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  Peng Ryan Huang 56  Xin Jin 57  ChienMing Huang 58  Will Gray 59  Kevin Duh