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 and affiliated MSE programs only (differs by course). After the initial registration period for each group, these restrictions will be lifted - on or about Dec. 3rd for undergraduate courses and January 9th for graduate courses. 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 were 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.

MWF 50 minutes, limit 19/section
See SIS class search for section times and restrictions.

601.104 (H)
CSCI-ETHS

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.

Sections meet during the first 8 weeks of the semester only.

Sec 01: Mon 4:30-6:00p
Sec 02: Mon 6:30-8:00p
Sec 03: Tue 4:30-6:00p
Sec 04: Tue 6:30-8:00p
limit 25/section, CS majors (no expiration)

601.124 (EH)
CSCI-ETHS

THE ETHICS OF ARTIFICIAL INTELLIGENCE & AUTOMATION (3) Lopez-Gonzalez

The expansion of artificial intelligence (AI)-enabled use cases across a broad spectrum of domains has underscored the benefits and risks of AI. This course will address the various ethical considerations engineers need to engage with to build responsible and trustworthy AI-enabled autonomous systems. Topics to be covered include: values-based decision making, ethically aligned design, cultural diversity, safety, bias, AI explainability, privacy, AI regulation, the ethics of synthetic life, and the future of work. Case studies will be utilized to illustrate real-world applications. Students will apply learned material to a group research project on a topic of their choice.
This new course may be used as an alternative course to satisfy the CS Ethics requirement.

Sec 01: MW 12-1:15p
Sec 02: MW 3-4:15p
limit 25/section, CS majors only (no expiration)

601.220 (E)

INTERMEDIATE PROGRAMMING (4) Darvish, Hovemeyer

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. The course includes usage of Linux tools for file manipulation, editing and programming, and git for versioning. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages.

Prereq: 500.132/133/134 OR (C+/S*/S** or better grade in 500.112/113/114) or AP CS or equivalent.

Sec 01 (Darvish): MWF 10:00-11:15
Sec 02 (Darvish): MWF 12:00-1:15
Sec 03 (Hovemeyer): MWF 1:30-2:45
Sec 04 (Hovemeyer): MWF 3:00-4:15
limit 34/sections 1, 3, 4; limit 70 section 2
CS/CE/EE majors/minors only until 12/3

601.226 (EQ)

DATA STRUCTURES (4) Madooei, Simari

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: 500.132 OR (C+/S*/S** or better grade in 500.112 or 601.220) or AP CS credit or equivalent.

Sec 01 (Madooei): MWF 12-1:15p
Sec 02 (Simari): MWF 1:30-2:45p
limit 90/section
CS/CE/CIS/Robotics majors/minors only until 12/3

601.229 (E)

COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer

This course covers modern computer systems from a software perspective. Topics include binary data representation, machine arithmetic, assembly language, computer architecture, performance optimization, memory hierarchy and cache organization, virtual memory, Unix systems programming, network programming, and concurrency. Hardware and software interactions relevant to computer security are highlighted. Students will gain hands-on experience with these topics in a series of programming assignments.

Prereq: 601.220.

MWF 10a, limit 120
CS/CE majors/minors only until 12/3

601.230 (EQ)

MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE (4) More

This course provides an introduction to mathematical reasoning and discrete structures relevant to computer science. Topics include propositional and predicate logic, proof techniques including mathematical induction, sets, relations, functions, recurrences, counting techniques, simple computational models, asymptotic analysis, discrete probability, graphs, trees, and number theory.

Pre/co-req: Gateway Computing (500.112/113/114/132/133/134 or AP CS or 601.220). Students can get credit for at most one of EN.601.230 or EN.601.231.

Sec 01: MWF 9-9:50a, W 4:30-5:20p
Sec 02: MWF 9-9:50a, W 4:30-5:20p
Sec 03: MWF 9-9:50a, W 6-6:50
Sec 04: MWF 9-9:50a, W 6-6:50
Sec 05: MWF 9-9:50a, Th 4:30-5:20
Sec 06: MWF 9-9:50a, Th 4:30-5:20
Sec 07: MWF 9-9:50a, Th 6-6:50
Sec 08: MWF 9-9:50a, Th 6-6:50
limit 20/section, CS/CE majors only (no expiration)

601.290 (E)
CSCI-TEAM

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.

MW 3-4:15
limit 75, CS majors/minors only until 12/3

601.350 (E)
CSCI-APPL

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
limit 35, CS/CE majors/minors + BME majors only until 12/3

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
limit 5

601.404 (E)
NEW COURSE!

BRAIN & COMPUTATION (1) Kosaraju

Computational and network aspects of the brain are explored. The topics covered include structure, operation and connectivity of neurons, general network structure of the neural system, and the connectivity constraints imposed by pre- and post-natal neural development and the desirability of network consistency within a species. Both discrete and continuous aspects of neural computation are covered. Precise mathematical tools and analyses such as logic design, transient and steady state behavior of linear systems, and time and connectivity randomization are discussed. The concepts are illustrated with several applications. Memory formation from the synaptic level to the high level constructs are explored. Students are not expected to master any of the mathematical techniques but are expected to develop a strong qualitative appreciation of their power. Cerebellum, which has a simple network connectivity, will be covered as a typical system.

Prerequisites: linear algebra, differential equations, probability, and algorithms; or instructor approval. Students can receive credit for EN.601.404 or EN.601.604, but not both.

Tu 4:30-5:20p
limit 15

601.411 (E)
CSCI-TEAM

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.

Prerequisite: 660.410.

MW 12-1:15p
limit 20, CS/CE majors/minors only until 12/3

601.413 (E)
CSCI-SYST
NEW COURSE!

SOFTWARE DEFINED NETWORKS (3) Sabnani

Software-Defined Networks (SDN) enable programmability of data networks and hence rapid introduction of new services. They use software-based controllers to communicate with underlying hardware infrastructure and direct traffic on a network. This model differs from that of traditional networks, which use dedicated hardware devices (i.e., routers and switches) to control network traffic.
This technology is becoming a key part of web scale networks (at companies like Google and Amazon) and 5G/6G networks. Its importance will keep on growing. Many of today’s services and applications, especially when they involve the cloud, could not function without SDN. SDN allows data to move easily between distributed locations, which is critical for cloud applications.
A major focus will be on how this technology will be used in 5G and 6G Networks. The course will cover basics of SDN, ongoing research in this area, and the industrial deployments.

Prerequisite: EN.601.414/614. Students can receive credit for EN.601.413 or EN.601.613, but not both.

Tu 4:30-7p
limit 5, CS/CE majors/minors only until 12/3

601.419 (E)
CSCI-SYST

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 and EN.601.414 or permission. Students can only receive credit for one of 601.419/619.

MW 3-4:15p
limit 30, CS/CE majors minors only until 12/3

601.421 (E)
CSCI-SOFT, CSCI-TEAM

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]

Prereq: 601.226 & 601.220 & (EN.601.280 or EN.601.290). Students may receive credit for only one of 601.421/621.

MWF 4:30-5:20p
Sec 01: limit 45, pre-reqs enforced
Sec 02: limit 10, instructor approval required (pre-reqs not enforced)
CS/CE majors/minors only until 12/3

601.422 (E)
CSCI-SOFT

SOFTWARE TESTING & DEBUGGING (3) Darvish

Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. [Systems]

Pre-req: EN.601.290 or EN.601.421. Students may receive credit for 601.422 or 601.622, but not both.

MWF 1:30-2:20p
limit 35, CS/CE majors/minors only until 12/3

601.426 (EQ)
CSCI-THRY

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
limit 40, CS/CE majors/minors only until 12/3

601.433 (EQ)
CSCI-THRY

INTRO ALGORITHMS (3) Garg

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 601.230) or Perm. Req'd. Students may receive credit for only one of 601.433/633.

Sec 01: MW 12-1:15
Sec 02: MW 1:30-2:45
limit 90/section, CS/CE majors/minors only until 12/3
[Hackerman B17/Hodson 210 - dual projectors]

601.441 (E)
CSCI-THRY

BLOCKCHAINS AND CRYPTOCURRENCIES Jain & Green

This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. [Analysis] Students may receive credit for only one of 600.451, 601.441, 601.641.

Prereq: 601.226 and probability (EN.553.211/EN.553.310/EN.553.311/EN.553.420/EN.560.348).

MW 12-1:15
limit 24, CS/CE majors/minors only until 12/3

601.446 (E)
CSCI-THRY

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. [Analysis]

Pre-req: 600/601.226. Students may receive credit for 601.446 or 601.646, but not both.

TuTh 9:00-10:15
limit 25, CS/CE majors/minors only until 12/3

601.453 (E)
CSCI-APPL, CSCI-TEAM
NEW COURSE!

APPLICATIONS OF AUGMENTED REALITY (3) Martin-Gomez

This course is designed to expand the student’s augmented reality knowledge and introduce relevant topics necessary for developing more meaningful applications and conducting research in this field. The course addresses the fundamental concepts of visual perception and introduces non-visual augmented reality modalities, including auditory, tactile, gustatory, and olfactory applications. The following sessions discuss the importance of integrating user-centered design concepts to design meaningful augmented reality applications. A later module introduces the basic requirements to design and conduct user studies and guidelines on interpreting and evaluating the results from the studies. During the course, students conceptualize, design, implement and evaluate the performance of augmented reality solutions for their use in industrial applications, teaching and training, or healthcare settings. Homework in this course will relate to applying the theoretical methods used for designing, implementing, and evaluating augmented reality applications. Students may receive credit for only one of 601.453/653.

Prerequisites: EN.601.454/654.

TuTh 1:30-2:45p
limit 10, CS/CE majors/minors + CIS/Robotics minors only until 12/3

601.454 (E) CSCI-APPL

AUGMENTED REALITY (3) Azimi

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.

MW 3:00-4:15p 8:30-9:45a
limit 15, CS/CE majors/minors + CIS/Robotics minors only until 12/3

601.456 (E)
CSCI-APPL

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 who wish to use this course to satisfy the "Team" requirement should register for EN.601.496 instead. 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 only one of 601.456, 601.496, 601.656.
Note: Grad students taking this course should register for 600.656 instead.

TuTh 1:30-2:45
Sec 01: limit 14, CS/CE majors/minors + CompMed/CIS/Robotics minors only until 12/3
Sec 02: limit 6, instructor active approval only

601.461 (EQ)
CSCI-APPL

COMPUTER VISION (3) Jones & Katyal

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 9-10:15a
Sec 01: limit 25, CS/CE majors/minors only until 12/3
Sec 02: limit 5, CIS/Robotics minors only until 12/3

601.463 (E)
CSCI-APPL

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, calculus, probability. Students may receive credit for only one of 601.463/663.

TuTh 12-1:15
limit 10, CS/CE majors/minors + CIS/Robotics minors only until 12/3

601.464 (E)
CSCI-RSNG

ARTIFICIAL INTELLIGENCE (3) Haque

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

TuTh 3:00-4:15p 4:30-5:45p
Sec 01: limit 55, CS/CE majors/minors only until 12/3
Sec 02: limit 5, CIS/Robotics minors only until 12/3

601.466 (E)
CSCI-APPL

INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky

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 3-4:15
limit 40, CS/CE majors/minors only until 12/3
[Hackerman B17 tech]

601.471 (E)
CSCI-RSNG
NEW COURSE

NLP: SELF-SUPERVISED MODELS (3) Khashabi

The rise of massive self-supervised (pre-trained) models have transformed various data-driven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to self-supervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own self-supervised neural network models, using the Pytorch framework. Students may receive credit for EN.601.471 or EN.601.671, but not both.

Pre-reqs: EN.601.226, one of (EN.601.464/664, EN.601.465/665, EN.601.467/667, EN.601.468/668, EN.601.475/675), Linear Algebra, and Probability, as well as familiarity with Python/PyTorch.

TuTh 12-1:15p
limit 35, CS/CE majors/minors only until 12/3

601.475 (E)
CSCI-RSNG

MACHINE LEARNING (3) Arora

The goal of machine learning (a subfield of artificial intelligence) is the development of 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 and deep learning, as well as unsupervised learning frameworks, which include Expectation Maximization and graphical models. Homework assignments include both a heavy programming components as well as analytical questions that explore various machine learning concepts. This class will build on prerequisites that include probability, linear algebra, multivariate calculus and basic optimization. [Applications or Analysis] Students may receive credit for only one of 601.475/675.

Required course background: multivariable calculus, probability, linear algebra.

MWF 12-1:15p
Sec 01: limit 50, CS/CE majors/minors only until 12/3
Sec 02: limit 5, CompMed/CIS/Robotics minors only until 12/3

601.482 (E)
CSCI-RSNG

MACHINE LEARNING: DEEP LEARNING (4) Unberath

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.
The goal of this course is to introduce the basic concepts of deep learning (DL). The course will include a brief introduction to the basic theoretical and methodological underpinnings of machine learning, commonly used architectures for DL, DL optimization methods, DL programming systems, and specialized applications to computer vision, speech understanding, and robotics.
Students will be expected to solve several DL problems on standardized data sets, and will be given the opportunity to pursue team projects on topics of their choice. [Applications]

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.

MW 4:30-5:45p, F 4:30-5:20p
Sec 01: limit 45, CS/CE majors/minors only until 12/3
Sec 02: limit 5, CompMed/CIS/Robotics minors only until 12/3

601.491 (E)
CSCI-APPL

HUMAN-ROBOT INTERACTION (3) STAFF

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
limit 15, CS/CE majors/minors + CIS/Robotics minors only until 12/3

601.496 (E)
CSCI-APPL, CSCI-TEAM

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
limit 12, CS majors + CompMed/CIS/Robotics minors only until 12/3

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 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 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.

601.604 (E)
NEW COURSE!

BRAIN & COMPUTATION (1) Kosaraju

Computational and network aspects of the brain are explored. The topics covered include structure, operation and connectivity of neurons, general network structure of the neural system, and the connectivity constraints imposed by pre- and post-natal neural development and the desirability of network consistency within a species. Both discrete and continuous aspects of neural computation are covered. Precise mathematical tools and analyses such as logic design, transient and steady state behavior of linear systems, and time and connectivity randomization are discussed. The concepts are illustrated with several applications. Memory formation from the synaptic level to the high level constructs are explored. Students are not expected to master any of the mathematical techniques but are expected to develop a strong qualitative appreciation of their power. Cerebellum, which has a simple network connectivity, will be covered as a typical system.

Required course background: linear algebra, differential equations, probability, and algorithms; or instructor approval. Students can receive credit for EN.601.404 or EN.601.604, but not both.

Tu 4:30-5:20p
limit 15, P/F only, CS grads

601.611

CS INNOVATION AND ENTREPRENEURSHIP II Dahbura & Aronhime

Graduate level version of EN.601.411 (see for description)

Prerequisites: 660.410.

MW 12-1:15p
limit 5, active approval by instructor required

601.613 (E)
CSCI-SYST
NEW COURSE!

SOFTWARE DEFINED NETWORKS (3) Sabnani

Software-Defined Networks (SDN) enable programmability of data networks and hence rapid introduction of new services. They use software-based controllers to communicate with underlying hardware infrastructure and direct traffic on a network. This model differs from that of traditional networks, which use dedicated hardware devices (i.e., routers and switches) to control network traffic. This technology is becoming a key part of web scale networks (at companies like Google and Amazon) and 5G/6G networks. Its importance will keep on growing. Many of today’s services and applications, especially when they involve the cloud, could not function without SDN. SDN allows data to move easily between distributed locations, which is critical for cloud applications. A major focus will be on how this technology will be used in 5G and 6G Networks. The course will cover basics of SDN, ongoing research in this area, and the industrial deployments.

Required Course Background: computer networks. Students can receive credit for EN.601.413 or EN.601.613, but not both.

Tu 4:30-7p
limit 15, CS grads

601.619
CSCI-SYST

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]

Required course background: EN.601.226 and EN.601.414/614 or permission. Students can only receive credit for one of 601.419/619.

MW 3-4:15
Sec 01: limit 30, CS + MSEM grads
Sec 02: limit 5, Data Science grads

601.621
CSCI-SOFT

OBJECT ORIENTED SOFTWARE ENGINEERING 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]

Required course background: intermediate programming, data structures, and experience in mobile or web app development. Students may receive credit for only one of 601.421/621.

MWF 4:30-5:20p
Sec 01: limit 20, instructor active approval only
CS + MSEM grads

601.622
CSCI-SOFT

SOFTWARE TESTING & DEBUGGING (3) Darvish

Studies show that testing can account for over 50% of software development costs. This course presents a comprehensive study of software testing, principles, methodologies, tools, and techniques. Topics include testing principles, coverage (graph coverage, logic coverage, input space partitioning, and syntax-based coverage), unit testing, higher-order testing (integration, system-level, acceptance), testing approaches (white-box, black-box, grey-box), regression testing, debugging, delta debugging, and several specific types of functional and non-functional testing as schedule/interest permits (GUI testing, usability testing, security testing, load/performance testing, A/B testing etc.). For practical topics, state- of-the-art tools/techniques will be studied and utilized. [Systems]

Pre-req: EN.601.290 or EN.601.421 or EN.601.621. Students may receive credit for 601.422 or 601.622, but not both.

MWF 1:30-2:20p
limit 20, CS + MSEM grads

601.626
CSCI-THRY

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
limit 35, CS + MSEM grad students

601.633
CSCI-THRY

INTRO ALGORITHMS Garg

Same as 601.433, for graduate students. [Analysis]

Prereq: 601.226 and (553.171/172 or 601.230 or 601.231) or Perm. Required. Students may receive credit for only one of 601.433/633.

TuTh 12-1:15p
sec 01: limit 55, CS grad students
sec 02: limit 35, MSEM, MSSI, Robotics, Data Science
[Hackerman B17/Hodson 210 - dual projectors]

601.641
CSCI-THRY

BLOCKCHAINS AND CRYPTOCURRENCIES Jain & Green

[Cross-listed in JHUISI.] Same as EN.601.441, for graduate students. [Analysis] Students may receive credit for only one of 600.451, 601.441, 601.641.

Required course background: 601.226 and probability (any course).

MW 12-1:15
limit 24, CS + MSEM + MSSI grads

601.646
CSCI-THRY

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. [Analysis]

Pre-req: Data Structures. Students may receive credit for 601.446 or 601.646, but not both.

TuTh 9-10:15
Sec 01: limit 20, CS grads
Sec 02: limit 5, Data Science Masters
[Sec 03: limit 5 - closed for now]

601.653 (E)
CSCI-APPL
NEW COURSE!

APPLICATIONS OF AUGMENTED REALITY (3) Martin-Gomez

This course is designed to expand the student’s augmented reality knowledge and introduce relevant topics necessary for developing more meaningful applications and conducting research in this field. The course addresses the fundamental concepts of visual perception and introduces non-visual augmented reality modalities, including auditory, tactile, gustatory, and olfactory applications. The following sessions discuss the importance of integrating user-centered design concepts to design meaningful augmented reality applications. A later module introduces the basic requirements to design and conduct user studies and guidelines on interpreting and evaluating the results from the studies. During the course, students conceptualize, design, implement and evaluate the performance of augmented reality solutions for their use in industrial applications, teaching and training, or healthcare settings. Homework in this course will relate to applying the theoretical methods used for designing, implementing, and evaluating augmented reality applications. Students may receive credit for only one of 601.453/653.

Required course background: intermediate programming (C/C++), data structures, linear algebra; EN.601.654 preferred.

TuTh 1:30-2:45p
Sec 01: limit 10, CS grads only
Sec 02: limit 10, MSEM + Robotics masters only

601.654
CSCI-APPL

AUGMENTED REALITY (3) Azimi

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.

MW 3:00-4:15p 8:30-9:45a
Sec 01: limit 15, CS grads only
Sec 02: limit 10, MSEM + Robotics masters only

601.656
CSCI-APPL

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 601.456/496/656.

TuTh 1:30-2:45
Sec 01: limit 10, CS grads only
Sec 02: limit 20, Robotics masters only
Sec 03: limit 20, instructor approval only

601.661
CSCI-APPL

COMPUTER VISION Jones & Katyal

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
Sec 01: limit 30, CS + MSEM grads
Sec 02: limit 10, Robotics + Data Science grads
[Sec 03: limit 10, closed for now]

601.663
CSCI-APPL

ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard

Same as 601.463, for graduate students. [Analysis]

Required course background: 601.226, linear algebra, calculus, probability. Students may receive credit for only one of 601.463/663.

TuTh 12-1:15
Sec 01: limit 10, CS + MSEM grads
Sec 02: limit 40, Robotics + Data Science masters
[Sec 03: limit 5, closed for now]

601.664
CSCI-RSNG

ARTIFICIAL INTELLIGENCE Haque

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 3:00-4:15p 4:30-5:45p
Sec 01: limit 40, CS + MSEM grads
Sec 02: limit 10, Robotics + Data Science grads
[Sec 03: limit 5, closed for now]

601.666
CSCI-APPL

INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky

Same material as 601.466, for graduate students. [Applications] Students may receive credit for at most one of 601.466/666.

Required course background: 601.226.

TuTh 3-4:15p
Sec 01: limit 30, CS + MSEM grads
Sec 02: limit 10, Data Science grads
[Hackerman B17 tech]

601.671 (E)
CSCI-RSNG
NEW COURSE

NLP: SELF-SUPERVISED MODELS (3) Khashabi

The rise of massive self-supervised (pre-trained) models have transformed various data-driven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to self-supervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own self-supervised neural network models, using the Pytorch framework. Students may receive credit for EN.601.471 or EN.601.671, but not both. Required course background: data structures, linear algebra, probability, familiarity with Python/PyTorch, natural language processing or machine learning.

Pre-reqs: one of EN.601.464/664, EN.601.465/665, EN.601.467/667, EN.601.468/668, EN.601.475/675.

TuTh 12-1:15p
limit 15

601.675
CSCI-RSNG

MACHINE LEARNING Arora

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 12-1:15p
Sec 01: limit 40, CS + MSEM grads
Sec 02: limit 20, Robotics + Data Science masters
[Sec 03: limit 10, closed for now]

601.682
CSCI-RSNG

MACHINE LEARNING: DEEP LEARNING Unberath

Same as 601.482, for graduate students. [Applications]

Required course background: data structures, probability and linear algebra; numerical optimization and Python recommended.

MW 4:30-5:45p, F 4:30-5:20p
Sec 01: limit 50, CS + MSEM grads
Sec 02: limit 15, Robotics + Data Science masters
[Sec 03: limit 10, closed for now]

601.691
CSCI-APPL

HUMAN-ROBOT INTERACTION STAFF

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
Sec 01: limit 15, CS grads
Sec 02: limit 5, MSEM + Robotics masters

601.740
CSCI-SYST

LANGUAGE-BASED SECURITY Cao

This course will introduce Language-based Security, an emerging field in cyber security that leverages techniques from compilers and program analysis for security-related problems. Topics include but are not limited to: Control-flow and data-flow graphs, Program slicing, Code property graph (CPG), and Control-flow integrity. Students are expected to read new and classic papers in this area and discuss them in class. [Systems]

Recommended Course Background: coursework in operating systems and preferably compilers.

TuTh 12:00-1:15
limit 25, CS, MSSI + MSEM grads

601.743
CSCI-APPL

ADVANCED TOPICS IN COMPUTER SECURITY Rubin

[Cross-listed in ISI] Topics will vary from year to year, but will focus mainly on network perimeter protection, host-level protection, authentication technologies, intellectual property protection, formal analysis techniques, intrusion detection and similarly advanced subjects. Emphasis in this course is on understanding how security issues impact real systems, while maintaining an appreciation for grounding the work in fundamental science. Students will study and present various advanced research papers to the class. There will be homework assignments and a course project. [Systems or Applications]

Prereq: college level security or crypto course; or permission of instructor.

Mon 1:30-4:00
limit 19, instructor active approval

EN.601.760

FFT IN GRAPHICS & VISION (3) Kazhdan

In this course, we will study the Fourier Transform from the perspective of representation theory. We will begin by considering the standard transform defined by the commutative group of rotations in 2D and translations in two- and three-dimensions, and will proceed to the Fourier Transform of the non-commutative group of 3D rotations. Subjects covered will include correlation of images, shape matching, computation of invariances, and symmetry detection. [Applications or Analysis]

Prereq: linear algebra and comfort with mathematical derivations.

MW 1:30-2:45
limit 20, CS grads only

601.763
CSCI-APPL

NEW COURSE!

ADVANCED TOPICS IN ROBOT PERCEPTION Katyal

The goal of this course is to explore machine learning and perception algorithms focused on robotic applications. Topics will include robot localization and mapping, pedestrian/obstacle detection/prediction, semantic segmentation, perception-based grasp planning, continual learning for perception algorithms and multimodal sensor fusion. This course will include introductions to the topics by the instructor followed by paper reading and discussions led by the students. In addition, this course will consist of an in-depth semester long project that will emphasize research skills including developing a hypothesis, conducting literature reviews, formulating the problem, defining, and conducting experiments and finally evaluating and reporting results. [Applications]

Required Course Background: Programming, Linear Algebra, Prob/Stat, Computer Vision and (Machine Learning or ML: Deep Learning).

Fr 10a-12:30p
Sec 01: limit 12, CS + Robotics grad students
[Sec 02: limit 5, instructor active approval, closed for now]

601.764
CSCI-APPL

NEW COURSE!

ADVANCED NLP: MULTILINGUAL METHODS Murray

This is a project based course focusing on the design and implementation of systems that scale Natural Language Processing methods beyond English. The course will cover both multilingual and cross-lingual methods with an emphasis on zero-shot and few-shot approaches, as well as ‘silver’ dataset creation. Modules will include Cross-Lingual Information Extraction & Semantics, Cross-Language Information Retrieval, Multilingual Question Answering, Multilingual Structured Prediction, Multilingual Automatic Speech Recognition, as well as other non-English centric NLP methods. Students will be expected to work in small groups and pick from one of the modules to create a model based on state-of-the-art methods covered in the class. The course will be roughly two-thirds lecture based and one-third students presenting project updates periodically throughout the semester. [Applications]

Prerequisite: 601.465/665 NLP; Machine Translation recommended.

TuTh 1:30-2:45
Sec 01: limit 20, CS grad students
Sec 02: limit 10, instructor active approval for non-CS HLT masters/CLSP PhD students

601.778 (formerly 600.678)

ADVANCED TOPICS IN CAUSAL INFERENCE (3) Shpitser

This course will cover advanced topics on all areas of causal inference, including learning causal effects, path-specific effects, and optimal policies from data featuring biases induced by missing data, confounders, selection, and measurement error, techniques for generalizing findings to different populations, complex probabilistic models relevant for causal inference applications, learning causal structure from data, and inference under interference and network effects. The course will feature a final project which would involve either an applied data analysis problem (with a causal inference flavor), a literature review, or theoretical work. [Analysis]

Pre-requisite: EN.601.477/677 or permission.

TuTh 1:30-2:45p
limit 20, CS grads only

601.783
CSCI-APPL

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
Sec 01: limit 35, CS + Cog Sci grads
Sec 02: limit 15, MSEM + Robotics masters

601.787
CSCI-RSNG

ADVANCED MACHINE LEARNING: MACHINE LEARNING FOR TRUSTWORTHY AI Liu

This course teaches advanced machine learning methods for the design, implementation, and deployment of trustworthy AI systems. The topics we will cover include but are not limit to different types of robust learning methods, fair learning methods, safe learning methods, and research frontiers in transparency, interpretability, privacy, sustainability, AI safety and ethics. Students will learn the state-of-the-art methods in lectures, understand the recent advances by critiquing research articles, and apply/innovate new machine learning methods in an application. There will be homework assignments and a course project.

Expected course background: 601.475/675 Machine Learning; recommended 601.476/676 ML: Data to Models and 601.482/682 Deep Learning.

MW 3-4:15
Sec 01: limit 25, CS grads
[Sec 02: limit 5, closed for now]

601.801

COMPUTER SCIENCE SEMINAR

Required for all CS PhD students. Strongly recommended for MSE students.

Only 1st & 2nd year PhD students should formally register.

TuTh 10:30-11:45
limit 90, P/F only

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, P/F only

601.809

PHD RESEARCH

See below for faculty section numbers.

601.810

DIVERSITY & INCLUSION IN COMPUTER SCIENCE & ENGINEERING Kazhdan

This reading seminar will focus on the question of diversity and inclusion in computer science (in particular) and engineering (in general). We aim to study the ways in which the curriculum, environment, and structure of computer science within academia perpetuates biases alienating female and minoritized students, and to explore possible approaches for diversifying our field. The seminar will meet on a weekly basis, readings will be assigned, and students will be expected to participate in the discussion.

Wed 4:30-5:45p
limit 8, P/F only

601.819

SELECTED TOPICS IN CLOUD COMPUTING AND NETWORKED SYSTEMS Ghorbani

Participants will read and discuss seminal and recent foundational research on cloud and networked systems.

W 4:30-5:45p
limit 12, P/F only

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
limit 10, P/F only

601.856

SEMINAR: MEDICAL IMAGE ANALYSIS Taylor & Prince

This weekly seminar will focus on research issues in medical image analysis, including image segmentation, registration, statistical modeling, and applications. It will also include selected topics relating to medical image acquisition, especially where they relate to analysis. The purpose of the course is to provide the participants with a thorough background in current research in these areas, as well as to promote greater awareness and interaction between multiple research groups within the University. The format of the course is informal. Students will read selected papers. All students will be assumed to have read these papers by the time the paper is scheduled for discussion. But individual students will be assigned on a rotating basis to lead the discussion on particular papers or sections of papers. Co-listed with 520.746.

Tu 3-4:15
limit 24, P/F only

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, P/F only

601.864

added!

SELECTED TOPICS IN MULTILINGUAL NATURAL LANGUAGE PROCESSING Yarowsky

This is a weekly reading group focused on Natural Language Processing (NLP) outside of English. Whereas methods have gotten very strong in recent years on English NLP tasks, many methods fail on other languages due to both linguistic differences as well as lack of available annotated resources. This course will focus on Cross-Language Information Retrieval, Cross-Lingual Information Extraction, Multilingual Semantics, Massively Multilingual Language Modeling, and other non-English NLP sub-fields. Students will be expected to read, discuss, and present papers. Required course background: EN.601.465/665.

M 1:30
limit 15

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.

Required course background: 600.465 or permission of instructor.

Wed 12-1:15
limit 15, P/F only

601.866

SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme

A seminar focussed on current research and survey articles on computational semantics.

Fr 10-10:50
limit 15, P/F only

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.

M 11-12
limit 15, P/F only

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
limit 80

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

650.624
CSCI-SYST

NETWORK SECURITY Johnston

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.
Required Course Background: EN.601.220, EN.601.226, EN.601.418 or equivalent.

TuTh 3-4:15p
limit 25, CS + MSSI + MSEM grads

650.631

ETHICAL HACKING Watkins

Cyber security affects every facet of industry and our government, and thus is now a threat to National Security. This course is designed to introduce students to the skills needed to defend computer network infrastructure by exposing them to the hands-on identification and exploitation of vulnerabilities in servers (i.e., Windows and Linux), wireless networks, websites, and cryptologic systems. These skills will be tested by having teams of students develop and participate in instructor lead capture-the-flag competitions. Also included are advanced topics such as shell coding, IDA Pro analysis, fuzzing, and writing or exploiting network-based applications or techniques such as web servers, spoofing, and denial of service.

Th 4:30-7p
limit 50, CS + MSSI + MSEM grads

650.640

MORAL & LEGAL FOUNDATIONS OF PRIVACY Matthew Welling

This course explores the ethical and legal underpinnings of the concept of privacy. It examines the nature and scope of the right to privacy by addressing fundamental questions such as: What is privacy? Why is privacy morally important? How is the right to privacy been articulated in constitutional law?

Tu 4:30-6:45p
limit 30, MSSI only
[tech classroom Hodson]

650.654

COMPUTER INTRUSION DETECTION Li

Intrusion detection supports the on-line monitoring of computer system activities and the detection of attempts to compromise normal services. This course starts with an overview of intrusion detection tasks and activities. Detailed discussion introduces a traditional classification of intrusion detection models, applications in host-centered and distributed environments, and various intrusion detection techniques ranging from statistical analysis to biological computing. This course serves as a comprehensive introduction of recent research efforts in intrusion detection and the challenges facing modern intrusion detection systems. Students will also be able to pursue in-depth study of special topics of interest in course projects.

MW 12-1:15p
limit 25, CS + MSSI + MSEM grads

650.667

MOBILE DEVICE FORENSICS Leschke

Wed 6:30-9p
limit 40

650.672

SECURITY ANALYTICS Zhang

Security analytics refers to information technology solutions that gather and analyze security events to bring situational awareness and enable IT staff to understand and analyze events that pose the greatest risk. Increasingly, detecting and preventing cyber attacks require sophisticated use of data analytics and machine learning tools. This course will cover fundamental theories and methods in data science, modern security analytical tools, and practical use cases of security analytics. Students of this course learn concepts, tasks, and methods of data science; and how to apply data science to cyber security problems. Students also learn how to use modern software in security analytics.
Recommend Course Background: Basic knowledge of statistics; Either python or R programming skill (do not require both).

Fri 4:15 -6:45p
limit 25, CS + MSSI + MSEM grads

650.837

INFORMATION SECURITY PROJECTS Dahbura & Li

Open to MSSI students. Permission Required for non-MSSI students. All MSSI programs must include a project involving a research and development oriented investigation focused on an approved topic addressing the field of information security and assurance from the perspective of relevant applications and/or theory. There must be project supervision and approval involving a JHUISI affiliated faculty member. A project can be conducted individually or within a team-structured environment comprised of MSSI students and an advisor. A successful project must result in an associated report suitable for on-line distribution. When appropriate, a project can also lead to the development of a so-called "deliverable" such as software or a prototype system. Projects can be sponsored by government/industry partners and affiliates of the Information Security Institute, and can also be related to faculty research programs supported by grants and Contracts. Required for MSSI students on full-time status.

MW 11-11:50a
limit 55, MSSI only, P/F grades

650.840

INFORMATION SECURITY INDEPENDENT STUDY Li

Individual study in an area of mutual interest to a graduate student and a faculty member in the Institute.

P/F grades

Faculty section numbers for all independent type courses, undergraduate and graduate.

01 - Xin Li
02 - Rao Kosaraju (emeritus)
03 - Soudeh Ghorbani
04 - Russ Taylor (ugrad research use 517, not 507)
05 - Scott Smith
06 - Joanne Selinski
07 - Harold Lehmann [SPH]
08 - Ali Madooei
09 - Greg Hager (ugrad research use 517, not 507)
10 - Craig Jones
11 - Sanjeev Khudhanpur [ECE]
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 [BME]
20 - Michael Schatz
21 - Avi Rubin
22 - Matt Green
23 - Yinzhi Cao
24 - Raman Arora (ugrad research use 517, not 507)
25 - Rai Winslow [BME]
26 - Misha Kazhdan
27 - David Hovemeyer
28 - Ali Darvish
29 - Alex Szalay [Physics]
30 - Peter Kazanzides
31 - Jerry Prince [BME]
32 - Carey Priebe [AMS]
33 - Nassir Navab
34 - Rene Vidal [BME]
35 - Alexis Battle (ugrad research use 517, not 507) [BME]
36 - Emad Boctor (ugrad research use 517, not 507) [SOM]
37 - Mathias Unberath
38 - Ben VanDurme
39 - Jeff Siewerdsen
40 - Vladimir Braverman
41 - Suchi Saria
42 - Ben Langmead
43 - Steven Salzberg
44 - Jean Fan [BME]
45 - Liliana Florea [SOM]
46 - Casey Overby Taylor [SPH]
47 - Philipp Koehn
48 - Abhishek Jain
49 - Anton Dahbura (ugrad research use 517, not 507)
50 - Joshua Vogelstein [BME]
51 - Ilya Shpitser
52 - Austin Reiter
53 - Tamas Budavari [AMS]
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 [CLSP]
60 - Mihaela Pertea
61 - Archana Venkataraman [ECE]
62 - Matt Post [CLSP]
63 - Vishal Patel [ECE]
64 - Rama Chellappa [ECE]
65 - Mehran Armand [MechE]
66 - Jeremias Sulam [BME]
67 - Anqi Liu
68 - Yana Safanova
69 - Musad Haque
70 - Stephen Walli
71 - Gregory Falco [CaSE]
72 - Thomas Lippincott [CLSP]
73 - Joel Bader [BME]
74 - Daniel Khashabi
75 - Nicolas Loizou (AMS)
76 - Alejandro Martin Gomez
77 - Kenton Murray
78 - Ehsan Azimi [SoN]
79 - Krishan Sabnani