Below are the computer science course offerings for one semester. This list only includes courses that count without reservation towards CS program requirements. Undergraduate majors might also want to consult the list of non-department courses that may be used as "CS other" in accordance with established credit restrictions.
- See the calendar layout for a convenient listing of course times and room requests.
- Click here for a printable version of this table only.
All undergraduate courses except EN.500.112 will initially be listed as CS/CE majors/minors only, plus some affiliated minors for certain courses. All graduate courses will initially be listed as CS & affiliated MSE programs only (differs by course). After the initial registration period for each group, these restrictions will be lifted - on or about Nov 15th. Please be considerate of our faculty time and do not email them seeking permission to bypass these restrictions.
New Area Designators - CS course area designators are changed effective July 2019. Previously there were 3 designations - Analysis, Systems, Applications - and these still appear in the course descriptions below for grandfathering purposes. Going forward there are 5 areas and many courses have been reclassified. These areas will be implemented as POS (program of study) tags in SIS and are listed below each course number in the listings table. There are also 2 extra tags for undergraduates. Here are the new areas and tags:
- CSCI-APPL Applications
- CSCI-RSNG Reasoning
- CSCI-SOFT Software
- CSCI-SYST Systems
- CSCI-THRY Theory
- CSCI-TEAM Team (undergraduate only)
- CSCI-ETHS Ethics (undergraduate only)
Course Numbering Note - In order to be compliant with undergraduate students only in courses <=5xx and graduate students in courses >=6xx, we completely renumbered all the courses in the department in Fall 2017, with a 601 prefix instead of the old 600 prefix. Courses are listed here with new numbers only - note that some suffixes were changed as noted in bold. Grad students must take courses 601.6xx and above to count towards their degrees. Combined bachelors/masters students may count courses numbered 601.4xx towards their masters degree if taken before the undergrad degree was completed. [All co-listed 601.4xx/6xx courses are equivalent.]
Courses without end times are presumed to meet for 50 minute periods. Final room assignments will be available on the Registrar's website in January. Changes to the original SIS-posted schedule are noted in red.
500.112 (E) |
GATEWAY COMPUTING: JAVA (3) staff This course introduces fundamental programming concepts and techniques, and is intended for all who plan to develop computational artifacts or intelligently deploy computational tools in their studies and careers. Topics covered include the design and implementation of algorithms using variables, control structures, arrays, functions, files, testing, debugging, and structured program design. Elements of object-oriented programming, algorithmic efficiency and data visualization are also introduced. Students deploy programming to develop working solutions that address problems in engineering, science and other areas of contemporary interest that vary from section to section. Course homework involves significant programming. Attendance and participation in class sessions are expected. See SIS class search for sections. |
MWF 50 minutes, limit 19/section |
601.104 (H) |
COMPUTER ETHICS (1) Leschke Students will examine a variety of topics regarding policy, legal, and moral issues related to the computer science profession itself and to the proliferation of computers in all aspects of society, especially in the era of the Internet. The course will cover various general issues related to ethical frameworks and apply those frameworks more specifically to the use of computers and the Internet. The topics will include privacy issues, computer crime, intellectual property law -- specifically copyright and patent issues, globalization, and ethical responsibilities for computer science professionals. Work in the course will consist of weekly assignments on one or more of the readings and a final paper on a topic chosen by the student and approved by the instructor. |
Sec 01: Wed 4:30-6:30, alternate weeks (start 1/29)
|
601.220 (E)
|
INTERMEDIATE PROGRAMMING (4) staff This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover low-level programming techniques, as well as object-oriented class design, and the use of class libraries. Specific topics include pointers, dynamic memory allocation, polymorphism, overloading, inheritance, templates, collections, exceptions, and others as time permits. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages. Prereq: C+ or better grade in AP CS, 601.107, 600.112, 500.112/113/114, 580.200 or equivalent. |
Sec 01 (Hovemeyer): MWF 12-1:15 |
601.226 (EQ) |
DATA STRUCTURES (4) Madooei This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prereq: C+ or better grade in 500.112, 601.107, or 601.220. |
Sec 01: MWF 12-1:15 |
601.229 (E) |
COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer/Jin We study the design and performance of a variety of computer systems from simple 8-bit micro-controllers through 32/64-bit 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, super-scalar architectures, memory management units, etc. Along the way we'll study several typical instruction set architectures and review concepts such as interrupts, hardware and software exceptions, serial and other peripheral communications protocols, etc. A number of programming projects, frequently done in assembly language and using various processor simulators, round out the course. Prereq: 601.220. |
Sec 01: MWF 10 Sec 02: MW 3-4:15 |
601.231 (EQ) |
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, NP-completeness, and randomization. Students may not receive credit for 600.271/601.231 and 600.471/601.631 for the same degree. Prereq: 553.171/172. |
TuTh 1:30-2:45
|
601.290 (E) |
USER INTERFACES AND MOBILE APPLICATIONS (3) Selinski This course will provide students with a rich development experience, focused on the design and implementation of user interfaces and mobile applications. A brief overview of human computer interaction will provide context for designing, prototyping and evaluating user interfaces. Students will invent their own mobile applications and implement them using the Android SDK, which is JAVA based. An overview of the Android platform and available technologies will be provided, as well as XML for layouts, and general concepts for effective mobile development. Students will be expected to explore and experiment with outside resources in order to learn technical details independently. There will also be an emphasis on building teamwork skills, and on using modern development techniques and tools. [Oral] Prereq: 601.220 and 601.226. |
TuTh 3-4:15 |
601.295 (E) CANCELED |
DEVELOPING HEALTH IT APPLICATIONS (3) Overby & Zach-Doughty This course is a project-based introduction to working on successful projects in health care. In the first half of the term, students perform reading and homework assignments designed to introduce: (1) the context of health care delivery and health IT, (2) techniques to overcome challenges to conducting health care data analyses, and (3) techniques to design meaningful applications around health care data. In the second half of the term, students work in small groups to solve a real-world problem of their choosing. Includes exercises in written and oral communication and team building. [Oral starting 2019] Prereq: 601.220 and 601.226. |
CANCELED |
601.315 (E) |
DATABASES (3) More Introduction to database management systems and database design, focusing on the relational and object-oriented 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: 600/601.226. Students may receive credit for only one of 601.315/415/615. |
TuTh 9-10:15 |
601.320 (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 cutting-edge 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 multi-core processors. The course will examine different forms of parallelism in four sections. These are: (1) massive data-parallel computations with Hadoop!; (2) programming compute clusters with MPI; (3) thread-level parallelism in Java; and, (4) GPGPU parallel programming with NVIDIA's Cuda. Each section will be approximately 3 weeks and each section will involve a programming project. The course is also suitable for undergraduate and graduate students from other science and engineering disciplines that have prior programming experience. [Systems] Prereq: 601.226 and 601.229. Students may receive credit for at most one of 601.320/420/620. |
MW 4:30-5:45 |
601.350 (E) |
GENOMIC DATA SCIENCE (3) Salzberg [Formerly Intro to Genomic Research] This course will use a project-based 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 high-profile 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 3-4:15 |
601.356 (E) |
COMPUTER INTEGRATED SURGERY SEMINAR (1) Taylor Lecture only version of 601.456 (no project). Prereq: 601.455 or perm req'd. Students may receive credit for 600.356 or 600.456, but not both. |
TuTh 1:30-2:45 |
601.402 (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 computer-based patient records, clinical practice guidelines, and region-wide 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 5 weeks: Feb 3 - Mar 4 |
MW 4:30-5:45 |
601.411 (E) |
CS INNOVATION AND ENTREPRENEURSHIP II (3) Dahbura & Aronhime This course is the second half of a two-course sequence and is a continuation of course 660.410.01, CS Innovation and Entrepreneurship, offered by the Center for Leadership Education (CLE). In this sequel course the student groups, directed by CS faculty, will implement the business idea which was developed in the first course and will present the implementations and business plans to an outside panel made up of practitioners, industry representatives, and venture capitalists. [Oral] Prerequisite: 660.410. |
Th 4:30-7p |
601.414 (E) |
COMPUTER NETWORKS (3) Rubin/Kaptchuk 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 hands-on programming assignments, homeworks and two exams. [Systems] Prerequisites: EN.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
Sec 01 (Rubin): TuTh 9-10:15 |
601.419 (E) |
CLOUD COMPUTING (3) Ghorbani Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and low-latency requirements of real-time applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. [Systems] Prerequisites: EN.601.226 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming. |
MW 12-1:15 |
601.420 (E) |
PARALLEL PROGRAMMING (3) Burns More advanced version of 601.320. Students may receive credit for at most one of 601.320/420/620. [Systems]
Required course background: 601.226 and 601.229 or equiv. |
MW 4:30-5:45 |
601.421 (E) |
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Madooei This course covers object-oriented 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 object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. [(Systems or Applications), Oral] (https://www.jhu-oose.com) Prereq: 600/601.226 & 600.120/601.220. Students may receive credit for only one of 601.421/621. |
Lec: Tu 12-1:15 |
601.426 (EQ) |
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Functional, object-oriented, and other language features are studied independent of a particular programming language. Students become familiar with these features by implementing them. Most of the implementations are in the form of small language interpreters. Some type checkers and a small compiler will also be written. The total amount of code written will not be overly large, as the emphasis is on concepts. The ML programming language is the implementation language used. [Analysis] Required course background: 601.226. Freshmen and sophomores by permission only. |
MW 1:30-2:45 |
601.433 (EQ) |
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 union-find); graph algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis. [Analysis] Prereq: 601.226 and (553.171/172 or 601.231) or Perm. Req'd. Students may receive credit for only one of 601.433/633. |
TuTh 12-1:15 |
601.436 (EQ) |
ALGORITHMIC GAME THEORY Dinitz This course provides an introduction to algorithmic game theory: the study of games from the perspective of algorithms and theoretical computer science. There will be a particular focus on games that arise naturally from economic interactions involving computer systems (such as economic interactions between large-scale networks, online advertising markets, etc.), but there will also be broad coverage of games and mechanisms of all sorts. Topics covered will include a) complexity of computing equilibria and algorithms for doing so, b) (in)efficiency of equilibria, and c) algorithmic mechanism design. Students may receive credit for 601.436 or 601.636, but not both. Pre-req: 601.433/633 or permission. [Analysis] Prereq: 600.433/633 or permission. |
TuTh 3-4:15 |
601.445 (E)
|
PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green [Co-listed with 650.445.] This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed 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 reverse-engineering undocumented cryptographic systems. [Systems] Prereq: EN.600/601.226 and EN.600.233/601.229. Students may receive credit for only one of 601.445/645. |
MW 12-1:15 |
601.446 (E) |
SKETCHING & INDEXING FOR SEQUENCES (3) Langmead Many of the world's largest and fastest-growing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing --- succinct and compressed indexes in particular -- which enables us to efficiently search inside very long strings, especially in highly repetitive texts. The course will involve significant programming projects. [Analysis] Pre-req: 600.120/601.220, 600/601.226. Students may receive credit for 601.446 or 601.646, but not both. |
TuTh 12-1:15 |
601.454 (E) CSCI-APPL |
AUGMENTED REALITY (3) Navab
Same as 601.654, for undergraduate students.
[Applications] Students may receive credit for only one of 601.454/654. Prerequisites: EN.601.220, EN.601.226, linear algebra. |
TuTh 9-10:15 |
601.456 (E) |
COMPUTER INTEGRATED SURGERY II (3) Taylor This weekly lecture/seminar course addresses similar material to 600.455, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.455, although it does not have to be. Grades are based both on the project and on classroom recitations. Students wishing to attend the weekly lectures as a 1-credit seminar should sign up for 601.356. [Applications, Oral]
Prereq: 601.455/655 or perm req'd. Students may receive credit for
601.456 or 601.656, but not both. |
TuTh 1:30-2:45 |
601.496 (E) |
COMPUTER INTEGRATED SURGERY II - TEAMS (3) Taylor This weekly lecture/seminar course addresses similar material to 600.455, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project in teams of at least 3 students, under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.455, although it does not have to be. Grades are based both on the project and on classroom recitations. Students who prefer to do individual projects must register for EN.601.456 instead. [Applications, Oral]
Prereq: 601.455/655 or perm req'd. Students may receive credit for
only one of 601.456, 601.496, 601.656. |
TuTh 1:30-2:45 |
601.459 (EQ) CSCI-THRY |
COMPUTATIONAL GEOMETRY Kazhdan This course will provide an introduction to computational geometry. It will cover a number of topics in two- and three-dimensions, including polygon triangulations and partitions, convex hulls, Delaunay and Voronoi diagrams, arrangements, and spatial queries. Time-permitting, we will also look at kD-trees, general BSP-trees, and quadtrees. [Analysis] Pre-req: 601.220, 601.226, 601.433/633. Students may receive credit for 601.459 or 601.659, but not both. |
MW 1:30-2:45 |
601.461 (EQ) |
COMPUTER VISION (3) Shen 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 3-D 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 9-10:15 |
601.463 (E) |
ALGORITHMS FOR SENSOR-BASED 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 human-machine systems. [Analysis] Prereq: 601.226, linear algebra, probability. Students may receive credit for only one of 601.463/663. |
TuTh 12-1:15 |
601.464 (E) |
ARTIFICIAL INTELLIGENCE (3) Koehn/Poliak The course situates the study of Artificial Intelligence (AI) first in the broader context of Philosophy of Mind and Cognitive Psychology and then treats in-depth methods for automated reasoning, automatic problem solvers and planners, knowledge representation mechanisms, game playing, machine learning, and statistical pattern recognition. The class is a recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as deduction, learning, and planning and navigation. Strong programming skills and a good grasp of the English language are expected; students will be asked to complete both programming assignments and writing assignments. The course will include a brief introduction to scientific writing and experimental design, including assignments to apply these concepts. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 |
Sec 01 (Koehn): TuTh 1:30-2:45,
limit 40 |
601.466 (E) |
INFORMATION RETRIEVAL & WEB AGENTS (3) staff An in-depth, hands-on study of current information retrieval techniques and their application to developing intelligent WWW agents. Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modeling, information visualization and usage pattern analysis. In addition, the course explores the range of additional language processing steps useful for template filling and information extraction from retrieved documents, focusing on recent, primarily statistical methods. The course concludes with a study of current issues in information retrieval and data mining on the World Wide Web. Topics include web robots, spiders, agents and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. [Applications] Required course background: 601.226. |
TuTh 12-1:15 |
601.475 (E) |
MACHINE LEARNING (3) Graff
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 pre-requisites that include probability, linear
algebra, multi-variate calculus and basic optimization. Students in
the course will develop a learning system for a final project.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Required course background: multivariable calculus, probability, linear algebra. |
MWF 4:30-5:45
|
601.482 (E) |
MACHINE LEARNING: DEEP LEARNING (4) staff
Deep learning (DL) has emerged as a powerful tool for solving
data-intensive learning problems such as supervised learning for
classification or regression, dimensionality reduction, and control. As
such, it has a broad range of applications including speech and text
understanding, computer vision, medical imaging, and perception-based
robotics. Pre-req: EN.601.226 and (AS.110.201 or AS.110.212 or EN.553.291) and (EN.553.310 EN.553.311 or EN.553.420 or EN.560.348); numerical optimization and Python recommended. |
MWF 8:30-9:45a |
601.486 (E) |
MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT (3) Unberath
The field of artificial intelligence (AI) has recently seen a
substantial increase in popularity, largely fueled by the successes
of training deep neural networks that achieve state-of-the-art
performance in a large variety of problems. These successes are not
limited to academic benchmarks but have started to impact our
everyday lives in the form of products such as Google Lens, Amazon
Alexa, and Tesla Autopilot. In order for such AI systems to succeed
we must consider its impact on everyday life, its overall
capabilities and performance, and the effectiveness of the human-AI
interaction. The importance of harmonic interplay between all these
components is dramatically highlighted by recent catastrophic events
in road transport and aviation. In this project-based course you
will work in teams of 3-5 students to 1) Identify a need with
high-impact implications on everyday life; 2) Conceptualize and
design an AI system targeting this need, and 3) Develop the AI
system by refining a demo-able prototype based on feedback received
during course presentations. Pre-req: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design). |
TuTh 1:30-2:45 |
EN.580.488 (E) |
FOUNDATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS II Karchin [BME cross-list, counts as CS upper level credit.] This course will introduce probabilistic modeling and information theory applied to biological sequence analysis, focusing on statistical models of protein families, alignment algorithms, and models of evolution. topics will include probability theory, score matrices, hidden Markov models, maximum likelihood, expectation maximization and dynamic programming algorithms. Homework assignments will require programming in Python. Foundations of Computational Biology I is not a prereq. [Analysis] Required course background: math through linear algebra and differential equations, at least one statistics and probability course, 580.221 or equiv., 601.226 or equiv. |
MW 4:30-5:45 |
601.491 (E) |
HUMAN-ROBOT INTERACTION (3) C. Huang This course is designed to introduce advanced students to research methods and topics in human-robot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledge of various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. [Applications] Pre-requisite: EN.601.220 and EN.601.226. |
TuTh 3-4:15 |
601.501 |
COMPUTER SCIENCE WORKSHOP An independent applications-oriented, computer science project done under the supervision and with the sponsorship of a faculty member in the Department of Computer Science. Computer Science Workshop provides a student with an opportunity to apply theory and concepts of computer science to a significant project of mutual interest to the student and a Computer Science faculty member. Permission to enroll in CSW is granted by the faculty sponsor after his/her approval of a project proposal from the student. Interested students are advised to consult with Computer Science faculty members before preparing a Computer Science Workshop project proposal. Permission of faculty sponsor is required. |
See below for faculty section numbers. |
601.503 |
UNDERGRADUATE INDEPENDENT STUDY Individual guided study for undergraduates, under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission of faculty sponsor is required.
|
See below for faculty section numbers. |
601.507 |
UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See below for faculty section numbers and whether to select 508 or 518. |
601.509 |
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit, which is the limit per semester. Permission of faculty sponsor is required. |
See below for faculty section numbers. |
601.517 |
GROUP UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
Only for faculty specifically marked below. |
601.520 |
SENIOR HONORS THESIS (3) For computer science majors only, a continuation of 601.519. Prerequisite: 601.519 |
See below for faculty section numbers. |
601.611 |
CS INNOVATION AND ENTREPRENEURSHIP II Dahbura & Aronhime Graduate level version of EN.601.411 (see for description) Prerequisites: 660.410. |
Th 4:30-7p |
601.614 |
COMPUTER NETWORKS Rubin/Kaptchuk Same as 601.414, for graduate students. [Systems] Required course background: EN.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
Sec 01 (Rubin): TuTh 9-10:15 |
601.619 |
CLOUD COMPUTING Ghorbani [Same as 601.419, for graduate students.] Clouds host a wide range of the applications that we rely on today. In this course, we study common cloud applications, traffic patterns that they generate, critical networking infrastructures that support them, and core networking and distributed systems concepts, algorithms, and technologies used inside clouds. We will also study how today's application demand is influencing the network’s design, explore current practice, and how we can build future's networked infrastructure to better enable both efficient transfer of big data and low-latency requirements of real-time applications. The format of this course will be a mix of lectures, discussions, assignments, and a project designed to help students practice and apply the theories and techniques covered in the course. [Systems] Prerequisites: EN.601.226 or permission. Students can only receive credit for one of 601.419/619. Recommended: a course in operating systems, networks or systems programming. |
MW 12-1:15 |
601.620 |
PARALLEL PROGRAMMING (3) Burns Same as 601.420, for graduate students. Students may receive credit for at most one of 601.320/420/620. [Systems]
Required course background: 601.226 and 601.229 or equiv. |
MW 4:30-5:45 |
601.621 |
OBJECT ORIENTED SOFTWARE ENGINEERING Madooei Same material as 601.421, for graduate students. [Systems or Applications] (https://www.jhu-oose.com) Required course background: Intermediate Programming & Data Structures. Students may receive credit for only one of 601.421/621. |
Lec: Tu 12-1:15 |
601.626 |
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Same as 601.426, for graduate stuents. Students may receive credit for only one of 601.426/626. [Analysis] Required course background: 601.226. |
MW 1:30-2:45 |
601.633 |
INTRO ALGORITHMS Braverman
Same as 601.433, for graduate students. [Analysis] Prereq: 601.226 and 553.171/172 or Perm. Req'd. Students may receive credit for only one of 601.433/633. |
TuTh 12-1:15 |
601.636 |
ALGORITHMIC GAME THEORY Dinitz Same as EN.601.436, for graduate students. Students may receive credit for 601.436 or 601.636, but not both. Pre-req: 601.433/633 or permission. [Analysis] Prereq: 600.433/633 or permission. |
TuTh 3-4:15 |
601.645
|
PRACTICAL CRYPTOGRAPHIC SYSTEMS Green [Co-listed 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 12-1:15 |
601.646 |
SKETCHING & INDEXING FOR SEQUENCES (3) Langmead Many of the world's largest and fastest-growing datasets are text, e.g. DNA sequencing data, web pages, logs and social media posts. Such datasets are useful only to the degree we can query, compare and analyze them. Here we discuss two powerful approaches in this area. We will cover sketching, which enables us to summarize very large texts in small structures that allow us to measure the sizes of sets and of their unions and intersections. This in turn allows us to measure similarity and find near neighbors. Second, we will discuss indexing --- succinct and compressed indexes in particular -- which enables us to efficiently search inside very long strings, especially in highly repetitive texts. The course will involve significant programming projects. [Analysis] Pre-req: Intermediate Programming & Data Structures. Students may receive credit for 601.446 or 601.646, but not both. |
TuTh 12-1:15 |
601.654 |
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, multi-modal 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 9-10:15 |
601.656 |
COMPUTER INTEGRATED SURGERY II Taylor Same as 601.456, for graduate students. [Applications] Prereq: 601.455/655 or perm req'd. Students may receive credit for only one of 601.456/656. |
TuTh 1:30-2:45 |
601.659 |
COMPUTATIONAL GEOMETRY Kazhdan This course will provide an introduction to computational geometry. It will cover a number of topics in two- and three-dimensions, including polygon triangulations and partitions, convex hulls, Delaunay and Voronoi diagrams, arrangements, and spatial queries. Time-permitting, we will also look at kD-trees, general BSP-trees, and quadtrees. [Analysis] Pre-req: 601.220, 601.226, 601.433/633. Students may receive credit for 601.459 or 601.659, but not both. |
MW 1:30-2:45 |
601.661 |
COMPUTER VISION Shen 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 9-10:15 |
601.663 |
ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard Same as 601.463, for graduate students. [Analysis] Required course background: 601.226, calculus, prob/stat. Students may receive credit for only one of 601.463/663. |
TuTh 12-1:15 |
601.664 |
ARTIFICIAL INTELLIGENCE Koehn Same as 601.464, for graduate students. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664 |
TuTh 1:30-2:45 |
601.666 |
INFORMATION RETRIEVAL & WEB AGENTS (3) staff Same material as 601.466, for graduate students. [Applications] Students may receive credit for at most one of 601.466/666. Required course background: 601.226. |
TuTh 12-1:15 |
601.675 |
MACHINE LEARNING Graff
Same as 601.475, for graduate students.
[Applications or Analysis] Students may receive credit for only one
of 601.475/675. Required course background: multivariable calculus, probability, linear algebra. |
MWF 4:30-5:45
|
601.682 |
MACHINE LEARNING: DEEP LEARNING staff
Same as 601.482, for graduate students. [Applications]
Required course background: data structures, probability and linear algebra; numerical optimization and Python recommended. |
MWF 8:30-9:45a |
601.686 |
MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT Unberath
The field of artificial intelligence (AI) has recently seen a
substantial increase in popularity, largely fueled by the successes
of training deep neural networks that achieve state-of-the-art
performance in a large variety of problems. These successes are not
limited to academic benchmarks but have started to impact our
everyday lives in the form of products such as Google Lens, Amazon
Alexa, and Tesla Autopilot. In order for such AI systems to succeed
we must consider its impact on everyday life, its overall
capabilities and performance, and the effectiveness of the human-AI
interaction. The importance of harmonic interplay between all these
components is dramatically highlighted by recent catastrophic events
in road transport and aviation. In this project-based course you
will work in teams of 3-5 students to 1) Identify a need with
high-impact implications on everyday life; 2) Conceptualize and
design an AI system targeting this need, and 3) Develop the AI
system by refining a demo-able prototype based on feedback received
during course presentations. Required course background: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design). |
TuTh 1:30-2:45 |
580.688 |
FOUNDATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS II Karchin [Cross-listed from BME - CS students can count as a CS course.] This course will introduce probabilistic modeling and information theory applied to biological sequence analysis, focusing on statistical models of protein families, alignment algorithms, and models of evolution. topics will include probability theory, score matrices, hidden Markov models, maximum likelihood, expectation maximization and dynamic programming algorithms. Homework assignments will require programming in Python. Foundations of Computational Biology I is not a prereq. [Analysis] Required course background: math through linear algebra and differential equations, at least one statistics and probability course, 580.221 or equiv., 601.226 or equiv. |
MW 4:30-5:45 |
601.691 |
HUMAN-ROBOT INTERACTION C. Huang This course is designed to introduce graduate students to research methods and topics in human-robot interaction (HRI), an emerging research area focusing on the design and evaluation of interactions between humans and robotic technologies. Students will (1) learn design principles for building and research methods of evaluating interactive robot systems through lectures, readings, and assignments, (2) read and discuss relevant literature to gain sufficient knowledge of various research topics in HRI, and (3) work on a substantial project that integrates the principles, methods, and knowledge learned in this course. [Applications] Required course background: EN.601.220 and EN.601.226. |
TuTh 3-4:15 |
601.717 |
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 semester-long projects with high potential future impact. Example areas include robust scalable infrastructure (distributed datacenters, cloud networking, scada systems), real-time performance (remote surgery, trading systems), hybrid networks (mesh networks, 3-4G/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 3-4:15 |
601.718 |
ADVANCED OPERATING SYSTEMS R. Huang Students will study advanced operating system topics and be exposed to recent developments in operating systems research. This course involves readings on classic and new papers. Topics include virtual memory management, synchronization and communication, file systems, protection and security, operating system structure and extension techniques, fault tolerance, and history and experience of systems programming. [Systems] Prereq: 600/601.318/418/618 or permission. |
TuTh 1:30-2:45 |
601.742 |
ADVANCED TOPICS IN CRYPTOGRAPHY (3) Jain [Cross-listed 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 1:30-4 |
580.743 |
ADV TOPICS IN GENOME DATA ANALYSIS Battle [Formerly 600.641/601.751] Genomic data is becoming available in large quantities, but understanding how genetics contributes to human disease and other traits remains a major challenge. Machine learning and statistical approaches allow us to automatically analyze and combine genomic data, build predictive models, and identify genetic elements important to disease and cellular processes. This course will cover current uses of statistical methods and machine learning in diverse genomic applications including new genomic technologies. Students will present and discuss current literature. Topics include personal genomics, integrating diverse genomic data types, new technologies such as single cell sequencing and CRISPR, and other topics guided by student interest. The course will include a project component with the opportunity to explore publicly available genomic data. Recommended Course Background: coursework in data science or machine learning. |
MW 3-4:15 |
580.745 |
MATHEMATICS OF DEEP LEARNING Vidal [cross-listed from BME, 1.5 credits only] The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. For example, a key issue is that the training problem is nonconvex, hence optimization algorithms are not guaranteed to return a global minima. Another key issue is that while the size of deep networks is very large relative to the number of training examples, deep networks appear to generalize very well to unseen examples and new tasks. This course will overview recent work on the theory of deep learning that aims to understand the interplay between architecture design, regularization, generalization, and optimality properties of deep networks. |
Fr 1-3p |
601.749 |
COMPUTATIONAL GENOMICS: APPLIED COMPARATIVE GENOMICS (3) Schatz The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm & final exam, class presentations, and a significant class project. [Applications] Prereq: familiarity with UNIX scripting and/or programming. |
MW 1:30-2:45 |
601.767 NEW COURSE! |
DEEP LEARNING FOR AUTOMATED DISCOURSE Sedoc The overall objective of this course is for students to learn about state-of-the-art research in dialog systems, particularly focused on deep learning methods. Students will also learn how to read and navigate academic literature. The class will be centered around a 2-3 person project, with presentations/demos at the end of the semester. Students are expected to read, write, and review workshop-level academic papers. [Applications] Required course background: EN.601.467/667 or [(EN.601.475/675 or EN.601.482/682) and (EN.601.465/665 or EN.601.468/668 or EN.601.765)] or permission. |
TuTh 3-4:15 |
601.783 |
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 non-linear filtering, geometry, energy function methods, markov random fields, conditional random fields, graphical models, probabilistic grammars, and deep neural networks. These are illustrated on a set of vision problems ranging from image segmentation, semantic segmentation, depth estimation, object recognition, object parsing, scene parsing, action recognition, and text captioning. [Analysis or Applications] Required course background: calculus, linear algebra (AS.110.201 or equiv.), probability and statistics (AS.550.311 or equiv.), and the ability to program in Python and C++. Background in computer vision (EN.601.461/661) and machine learning (EN.601.475/675) suggested but not required. |
TuTh 9-10:15 |
601.801 |
Required for all CS PhD students. Strongly recommended for MSE students. |
TuTh 10:30-12 |
601.803 |
MASTERS RESEARCH Independent research for masters or pre-dissertation PhD 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 |
601.808 |
SELECTED TOPICS IN CS EDUCATION Selinski/More This course will explore current issues and research in computer science education. Topics will be drawn from literature, news items, and participant experience. Current faculty and students wtih interests in academic careers are encouraged to attend. Permission required. |
limit 15 |
601.809 |
PHD RESEARCH |
See below for faculty section numbers. |
601.817 |
SELECTED TOPICS IN SYSTEMS RESEARCH R.Huang This course covers latest advances in the research of computer systems including operating systems, distributed system, mobile and cloud computing. Students will read and discuss recent research papers in top systems conferences. Each week, one student will present the paper and lead the discussion for the week. The focus topics covered in the papers vary semester to semester. Example topics include fault-tolerance, reliability, verification, energy efficiency, and virtualization. |
Fr 1-2:15 |
601.826 |
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, higher-order program analysis, and constraint systems. Students will be expected to present papers orally. |
Fri 11-12 |
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 |
601.856 |
SEMINAR: MEDICAL IMAGE ANALYSIS Taylor & Prince [Co-listed as 520.746] This weekly seminar will focus on research issues in medical image analysis, including image segmentation, registration, statistical modeling, and applications. It will also include selected topics relating to medical image acquisition, especially where they relate to analysis. The purpose of the course is to provide the participants with a thorough background in current research in these areas, as well as to promote greater awareness and interaction between multiple research groups within the University. The format of the course is informal. Students will read selected papers. All students will be assumed to have read these papers by the time the paper is scheduled for discussion. But individual students will be assigned on a rotating basis to lead the discussion on particular papers or sections of papers. Co-listed with 520.746. |
Tu 3-4:50 |
601.857 | SELECTED TOPICS IN COMPUTER GRAPHICS Kazhdan In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week. |
Tu 3-4:15 limit 8 |
601.865 |
SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Sedoc A reading group exploring important current research in the field and potentially relevant material from related fields. Enrolled students are expected to present papers and lead discussion. Required course background: 600.465 or permission of instructor. |
Wed 12-1:15 |
601.866 |
SELECTED TOPICS IN MEANING, TRANSLATION AND GENERATION OF TEXT VanDurme A seminar focussed on current research and survey articles on computational semantics. |
Fr 10-10:50 |
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. |
T 9:30-10:30 |
500.745 |
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Cowan, Whitcomb, Vidal, Etienne-Cummings Seminar series in robotics. Topics include: Medical robotics, including computer-integrated surgical systems and image-guided 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. Human-machine systems, including haptic and visual feedback, human perception, cognition and decision making, and human-machine collaborative systems. Cross-listed with Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering. |
Wed 12-1:30 |
520.702 |
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING Trmal CLSP seminar series, for any students interested in current topics in language and speech processing. |
Mon & Fri 12-1:15 |
530.707 |
ROBOT SYSTEM PROGRAMMING Whitcomb (see SIS) |
TuTh 4:30-5:45 |
Faculty section numbers for all independent type courses, undergraduate and graduate.
01 - Xin Li 02 - Rao Kosaraju 03 - Soudeh Ghorbani 04 - Russ Taylor (ugrad research use 517, not 507) 05 - Scott Smith 06 - Joanne Selinski 07 - Harold Lehmann 08 - Joao Sedoc 09 - Greg Hager (ugrad research use 517, not 507) 10 - Gregory Chirikjian 11 - Sanjeev Khudhanpur 12 - Yair Amir 13 - David Yarowsky 14 - Noah Cowan 15 - Randal Burns 16 - Jason Eisner (ugrad research use 517, not 507) 17 - Mark Dredze 18 - Michael Dinitz 19 - Rachel Karchin 20 - Michael Schatz 21 - Avi Rubin 22 - Matt Green 23 - Yinzhi Cao 24 - Raman Arora (ugrad research use 517, not 507) 25 - Rai Winslow 26 - Misha Kazhdan 27 - Chris Callison-Burch 28 - Ali Darvish 29 - Alex Szalay 30 - Peter Kazanzides 31 - Jerry Prince 32 - Carey Priebe 33 - Nassir Navab 34 - Rene Vidal 35 - Alexis Battle (ugrad research use 517, not 507) 36 - Emad Boctor (ugrad research use 517, not 507) 37 - Mathias Unberath 38 - Ben VanDurme 39 - Jeff Siewerdsen 40 - Vladimir Braverman 41 - Suchi Saria 42 - Ben Langmead 43 - Steven Salzberg 44 - Tal Linzen 45 - Liliana Florea 46 - Casey Overby Taylor 47 - Philipp Koehn 48 - Abhishek Jain 49 - Anton Dahbura (ugrad research use 517, not 507) 50 - Joshua Vogelstein 51 - Ilya Shpitser 52 - Austin Reiter 53 - Tamas Budavari 54 - Alan Yuille 55 - Peng Ryan Huang 56 - Xin Jin 57 - Chien-Ming Huang 58 - Will Gray Roncal (ugrad research use 517, not 507) 59 - Kevin Duh 60 - Mihaela Pertea [staff] 61 - Archana Venkataraman 62 - Matt Post