500.112 (E)
GATEWAY COMPUTING: JAVA (3) staff
This course introduces fundamental programming concepts andtechniques, and is intended for all who plan to develop computationalartifacts or intelligently deploy computational tools in their studiesand careers. Topics covered include the design and implementation ofalgorithms using variables, control structures, arrays, functions,files, testing, debugging, and structured program design. Elements ofobject-oriented programming, algorithmic efficiency and datavisualization are also introduced. Students deploy programming todevelop working solutions that address problems in engineering,science and other areas of contemporary interest that vary fromsection to section. Course homework involves significantprogramming. Attendance and participation in class sessions areexpected.
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, andmoral issues related to the computer science profession itself and to theproliferation of computers in all aspects of society, especially in the eraof the Internet. The course will cover various general issues related toethical frameworks and apply those frameworks more specifically to the useof computers and the Internet. The topics will include privacy issues,computer crime, intellectual property law -- specifically copyright andpatent issues, globalization, and ethical responsibilities for computerscience professionals. Work in the course will consist of weeklyassignments on one or more of the readings and a final paper on a topicchosen by the student and approved by the instructor.
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 hasunderscored the benefits and risks of AI. This course will address thevarious ethical considerations engineers need to engage with to buildresponsible and trustworthy AI-enabled autonomous systems. Topics tobe covered include: values-based decision making, ethically aligneddesign, cultural diversity, safety, bias, AI explainability, privacy,AI regulation, the ethics of synthetic life, and the future ofwork. Case studies will be utilized to illustrate real-worldapplications. 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 Cand C++. (Prior knowledge of these languages is not expected.) We willcover low-level programming techniques, as well as object-orientedclass design, and the use of class libraries. Specific topics includepointers, dynamic memory allocation, polymorphism, overloading,inheritance, templates, collections, exceptions, and others as timepermits. The course includes usage of Linux tools for filemanipulation, editing and programming, and git forversioning. Students are expected to learn syntax and some languagespecific features independently. Course work involves significantprogramming 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 datastructures and associated algorithms, including arrays, stacks,queues, linked lists, binary trees, heaps, balanced trees andgraphs. Other topics include sorting, hashing, Java generics, and unittesting. Course work involves both written homework and Javaprogramming 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.
Sec 01: MW 3-4:15, limit 75, CS majors/minors only until 12/3
Sec 02: Mon 3-4:15p, Wed 4:30-5:45p, limit 20, instructor approval
601.350 (E)
CSCI-APPL
GENOMIC DATA SCIENCE(3) Salzberg
[Formerly Intro to Genomic Research] Thiscourse will use a project-based approach to introduce undergraduatesto research in computational biology and genomics. During thesemester, students will take a series of large data sets, all derivedfrom recent research, and learn all the computational steps requiredto convert raw data into a polished analysis. Data challenges mightinclude the DNA sequences from a bacterial genome project, the RNAsequences from an experiment to measure gene expression, the DNA froma human microbiome sequencing experiment, and others. Topics may varyfrom year to year. In addition to computational data analysis,students will learn to do critical reading of the scientificl*terature by reading high-profile research papers that generatedgroundbreaking or controversial results. [Applications]Prerequisites: knowledge of the Unix operating system and programmingexpertise 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 SURGERYSEMINAR (1) Taylor
Lecture only version of 601.456 (noproject).
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 20
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 acontinuation of course 660.410.01, CS Innovation and Entrepreneurship,offered by the Center for Leadership Education (CLE).In this sequel course thestudent groups, directed by CS faculty, will implement the businessidea which was developed in the first course and willpresent the implementations and business plans to an outside panelmade up of practitioners, industry representatives, and venturecapitalists.
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
CANCELED
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 patternsthat they generate, critical networking infrastructures that supportthem, and core networking and distributed systems concepts,algorithms, and technologies used inside clouds. We will also studyhow today's application demand is influencing the network’sdesign, explore current practice, and how we can build future'snetworked infrastructure to better enable both efficient transfer ofbig 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 andapply 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 ORIENTEDSOFTWARE ENGINEERING (3) Madooei
This course covers object-oriented software constructionmethodologies and their application. The main component of the courseis a large team project on a topic of your choosing. Course topicscovered include object-oriented analysis and design, UML, designpatterns, refactoring, program testing, code repositories, teamprogramming, 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 60 45, pre-reqs enforced
Sec 02: limit 15 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 softwaredevelopment costs. This course presents a comprehensive study ofsoftware testing, principles, methodologies, tools, andtechniques. Topics include testing principles, coverage (graphcoverage, logic coverage, input space partitioning, and syntax-basedcoverage), unit testing, higher-order testing (integration,system-level, acceptance), testing approaches (white-box, black-box,grey-box), regression testing, debugging, delta debugging, and severalspecific types of functional and non-functional testing asschedule/interest permits (GUI testing, usability testing, securitytesting, load/performance testing, A/B testing etc.). For practicaltopics, state- of-the-art tools/techniques will be studied andutilized. [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 arestudied independent of a particular programming language.Students become familiar with these features by implementingthem. Most of the implementations are in the form of smalllanguage interpreters. Some type checkers and a small compilerwill also be written. The total amount of code written will notbe overly large, as the emphasis is on concepts. The MLprogramming language is the implementation language used. [Analysis]
Required course background: 601.226. Freshmen and sophom*ores 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 rigorousanalysis of their efficiency. topics include the basic definitions ofalgorithmic complexity (worst case, average case); basic tools such asdynamic programming, sorting, searching, and selection; advanced datastructures and their applications (such as union-find); graphalgorithms and searching techniques such as minimum spanning trees,depth-first search, shortest paths, design of online algorithms andcompetitive 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
Thiscourse will introduce students to cryptocurrencies and the mainunderlying technology of Blockchains. The course will start with therelevant background in cryptography and then proceed to cover therecent advances in the design and applications of blockchains. Thiscourse should primarily appeal to students who want to conductresearch in this area or wish to build new applications on top ofblockchains. It should also appeal to those who have a casual interestin 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).
TuTh 1:30-2:45 MW 12-1:15
limit 35 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 andanalyze them. Here we discuss two powerful approaches in this area.We will cover sketching, which enables us to summarize very largetexts in small structures that allow us to measure the sizes of setsand of their unions and intersections. This in turn allows us tomeasure similarity and find near neighbors. Second, we will discussindexing --- succinct and compressed indexes in particular -- whichenables us to efficiently search inside very long strings, especiallyin 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 realityknowledge and introduce relevant topics necessary for developing moremeaningful applications and conducting research in this field. Thecourse addresses the fundamental concepts of visual perception andintroduces non-visual augmented reality modalities, includingauditory, tactile, gustatory, and olfactory applications. Thefollowing sessions discuss the importance of integrating user-centereddesign concepts to design meaningful augmented reality applications. Alater module introduces the basic requirements to design and conductuser studies and guidelines on interpreting and evaluating the resultsfrom the studies. During the course, students conceptualize, design,implement and evaluate the performance of augmented reality solutionsfor their use in industrial applications, teaching and training, orhealthcare settings. Homework in this course will relate to applyingthe theoretical methods used for designing, implementing, andevaluating augmented reality applications. Students may receive creditfor only one of 601.453/653.
Prerequisites: EN.601.454/654.
TuTh 1:30-2:45p
limit 5 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 to600.455, but covers selected topics in greater depth. In addition to materialcovered in lectures/seminars by the instructor and other faculty, students areexpected to read and provide critical analysis/presentations of selectedpapers in recitation sessions. Students taking this course are required toundertake and report on a significant term project under the supervision ofthe instructor and clinical end users. Typically, this project is an extensionof the term project from 600.455, although it does not have to be. Grades arebased both on the project and on classroom recitations. Students who wish to use this course tosatisfy the "Team" requirement should register for EN.601.496 instead. Students wishing toattend 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 fundamentalmethods in computer vision from a computational perspective. Methodsstudied include: camera systems and their modelling, computation of3-D geometry from binocular stereo, motion, and photometric stereo;and object recognition. Edge detection and color perception arecovered as well. Elements of machine vision and biological vision arealso 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 roboticsystems for navigating in an environment from an algorithmicperspective. It will cover basic kinematics, configuration spaceconcepts, motion planning, and localization and mapping. It willdescribe these concepts in the context of the ROS software system, andwill present examples relevant to mobile platforms, manipulation,robotics surgery, and human-machine systems. [Analysis]
Prereq: 601.226, linear algebra, calculus, probability. Students may receivecredit 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) firstin the broader context of Philosophy of Mind and Cognitive Psychologyand then treats in-depth methods for automated reasoning, automaticproblem solvers and planners, knowledge representation mechanisms,game playing, machine learning, and statistical pattern recognition.The class is a recommended for allscientists and engineers with a genuine curiosity about thefundamental obstacles to getting machines to perform tasks such asdeduction, learning, and planning and navigation. Strong programmingskills and a good grasp of the English language are expected; studentswill be asked to complete both programming assignments and writingassignments. The course will include a brief introduction toscientific writing and experimental design, including assignments toapply 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 115 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 andtheir application to developing intelligent WWW agents. Topics include acomprehensive study of current document retrieval models, mail/news routingand filtering, document clustering, automatic indexing, query expansion,relevance feedback, user modeling, information visualization and usage patternanalysis. In addition, the course explores the range of additional languageprocessing steps useful for template filling and information extraction fromretrieved documents, focusing on recent, primarily statistical methods. Thecourse concludes with a study of current issues in information retrieval anddata mining on the World Wide Web. Topics include web robots, spiders, agentsand search engines, exploring both their practical implementation and theeconomic 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 asfamiliarity 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 algorithmsthat can learn from data to improve their performance. This courseintroduces the foundational concepts of modern Machine Learning,including core principles, popular algorithms and modeling platforms.This will include both supervised learning, which includes popularalgorithms like SVMs, logistic regression and deep learning, as wellas unsupervised learning frameworks, which include ExpectationMaximization and graphical models. Homework assignments include both aheavy programming components as well as analytical questions thatexplore various machine learning concepts. This class will build onprerequisites that include probability, linear algebra, multivariatecalculus and basic optimization. [Applications or Analysis] Students may receive credit for only one of 601.475/675.
Pre-reqs: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Students may receive credit for only one of 601.475/675.
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 solvingdata-intensive learning problems such as supervised learning forclassification or regression, dimensionality reduction, and control. Assuch, it has a broad range of applications including speech and textunderstanding, computer vision, medical imaging, and perception-basedrobotics.
The goal of this course is to introduce the basicconcepts of deep learning (DL). The course will include a briefintroduction to the basic theoretical and methodological underpinningsof machine learning, commonly used architectures for DL, DLoptimization methods, DL programming systems, and specializedapplications to computer vision, speech understanding, and robotics.
Students will be expected to solve several DL problems onstandardized data sets, and will be given the opportunity to pursueteam 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 or EN.553.311 or EN.553.420 or EN.560.348) and Calc III; 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) Stiber
This course is designed to introduce advanced students to researchmethods and topics in human-robot interaction (HRI), an emergingresearch area focusing on the design and evaluation of interactionsbetween humans and robotic technologies. Students will (1) learndesign principles for building and research methods of evaluatinginteractive robot systems through lectures, readings, and assignments,(2) read and discuss relevant literature to gain sufficient knowledgeof various research topics in HRI, and (3) work on a substantialproject that integrates the principles, methods, and knowledge learnedin 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 to600.455, but covers selected topics in greater depth. In addition to materialcovered in lectures/seminars by the instructor and other faculty, students areexpected to read and provide critical analysis/presentations of selectedpapers in recitation sessions. Students taking this course are required toundertake and report on a significant term project in teams of at least 3 students, under the supervision ofthe instructor and clinical end users. Typically, this project is an extensionof the term project from 600.455, although it does not have to be. Grades arebased 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 doneunder the supervision and with the sponsorship of a faculty member inthe Department of Computer Science. Computer Science Workshop providesa student with an opportunity to apply theory and concepts of computerscience to a significant project of mutual interest to the student anda Computer Science faculty member. Permission to enroll in CSW isgranted by the faculty sponsor after his/her approval of a projectproposal from the student. Interested students are advised to consultwith Computer Science faculty members before preparing a ComputerScience Workshop project proposal. Permission of faculty sponsor isrequired.
See below for facultysection numbers.
601.503
UNDERGRADUATE INDEPENDENT STUDY
Individual guided study for undergraduates, under the direction of afaculty member in the department. The program of study, including thecredit to be assigned, must be worked out in advance between thestudent and the faculty member involved. Permission of faculty sponsoris required.
See below for facultysection numbers.
601.507
UNDERGRADUATE RESEARCH
Independent research for undergraduates under the directionof a faculty member in the department. The program of research,including the credit to be assigned, must be worked out inadvance 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 facultymember in the department. The program of study must be worked out in advance between the student and thefaculty member involved. Students may not receive credit for work that theyare paid to do. As a rule of thumb, 40 hours of work is equivalent to onecredit, 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 directionof a faculty member in the department. This course has a weeklyresearch group meeting that students are expected to attend. The program of research,including the credit to be assigned, must be worked out inadvance 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 20, 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 30 15, CS grads
601.619
CSCI-SYST
CANCELED
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 patternsthat they generate, critical networking infrastructures that supportthem, and core networking and distributed systems concepts,algorithms, and technologies used inside clouds. We will also studyhow today's application demand is influencing the network’sdesign, explore current practice, and how we can build future'snetworked infrastructure to better enable both efficient transfer ofbig 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 andapply 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 constructionmethodologies and their application. The main component of the courseis a large team project on a topic of your choosing. Course topicscovered include object-oriented analysis and design, UML, designpatterns, refactoring, program testing, code repositories, teamprogramming, 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 softwaredevelopment costs. This course presents a comprehensive study ofsoftware testing, principles, methodologies, tools, andtechniques. Topics include testing principles, coverage (graphcoverage, logic coverage, input space partitioning, and syntax-basedcoverage), unit testing, higher-order testing (integration,system-level, acceptance), testing approaches (white-box, black-box,grey-box), regression testing, debugging, delta debugging, and severalspecific types of functional and non-functional testing asschedule/interest permits (GUI testing, usability testing, securitytesting, load/performance testing, A/B testing etc.). For practicaltopics, state- of-the-art tools/techniques will be studied andutilized. [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 PROGRAMMINGLANGUAGES (3) Smith
Same as 601.426, for graduatestuents. 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).
TuTh 1:30-2:45 MW 12-1:15
limit 55 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 usto 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 realityknowledge and introduce relevant topics necessary for developing moremeaningful applications and conducting research in this field. Thecourse addresses the fundamental concepts of visual perception andintroduces non-visual augmented reality modalities, includingauditory, tactile, gustatory, and olfactory applications. Thefollowing sessions discuss the importance of integrating user-centereddesign concepts to design meaningful augmented reality applications. Alater module introduces the basic requirements to design and conductuser studies and guidelines on interpreting and evaluating the resultsfrom the studies. During the course, students conceptualize, design,implement and evaluate the performance of augmented reality solutionsfor their use in industrial applications, teaching and training, orhealthcare settings. Homework in this course will relate to applyingthe theoretical methods used for designing, implementing, andevaluating augmented reality applications. Students may receive creditfor 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 15 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 AugmentedReality. It reviews its basic definitions, principles andapplications. It then focuses on Medical Augmented Reality and itsparticular requirements. The course also discusses the main issues ofcalibration, tracking, multi-modal registration, advance visualizationand display technologies. Homework in this course will relate to themathematical methods used for calibration, tracking and visualizationin medical augmented reality. Students may also be asked to readpapers and implement various techniques within groupprojects. [Applications] Students may receive credit for 600.484 or 600.684, butnot 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 receivecredit 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 of601.463/663.
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 60 40, CS + MSEM grads
Sec 02: limit 30 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 25 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 (calc III), prob/stat, linear algebra, intro computing.
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 Stiber
This course is designed to introduce graduate students to researchmethods and topics in human-robot interaction (HRI), an emergingresearch area focusing on the design and evaluation of interactionsbetween humans and robotic technologies. Students will (1) learndesign principles for building and research methods of evaluatinginteractive robot systems through lectures, readings, and assignments,(2) read and discuss relevant literature to gain sufficient knowledgeof various research topics in HRI, and (3) work on a substantialproject that integrates the principles, methods, and knowledge learnedin 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
601.742
CSCI-THRY
ADDED!
ADVANCED TOPICS INCRYPTOGRAPHY (3) Jain
[Cross-listed in ISI] This course will focus on advanced cryptographictopics with an emphasis on open research problems and studentpresentations. [Analysis]
Prereq: 600.442 or 600.472 or permission.
Tu 4-7p
limit 15
601.743
CSCI-APPL
ADVANCED TOPICS IN COMPUTER SECURITY Rubin
[Cross-listed in ISI] Topics will vary fromyear to year, but will focus mainly on network perimeter protection,host-level protection, authentication technologies, intellectualproperty protection, formal analysis techniques, intrusion detectionand similarly advanced subjects. Emphasis in this course is onunderstanding how security issues impact real systems, whilemaintaining an appreciation for grounding the work in fundamentalscience. Students will study and present various advanced researchpapers to the class. There will be homework assignments and a courseproject. [Systems or Applications]
Prereq: college level security or crypto course; or permission of instructor.
Mon 1:30-4:00
limit 19, instructor active approval
580.743
CSCI-APPL
ADV TOPICS IN GENOME DATA ANALYSIS Battle
[Formerly 600.641/601.751] Genomic data is becoming available in large quantities, butunderstanding how genetics contributes to human disease and othertraits remains a major challenge. Machine learning and statisticalapproaches allow us to automatically analyze and combine genomic data,build predictive models, and identify genetic elements important todisease and cellular processes. This course will cover current uses ofstatistical methods and machine learning in diverse genomicapplications including new genomic technologies. Students will presentand discuss current literature. Topics include personal genomics,integrating diverse genomic data types, new technologies such assingle cell sequencing and CRISPR, and other topics guided by studentinterest. The course will include a project component with theopportunity to explore publicly available genomic data.
Recommended Course Background: coursework in data science ormachine learning.
MW 3-4:15p
limit 20
EN.601.760
(3) Kazhdan
In this course, we will study the Fourier Transform from theperspective of representation theory. We will begin by considering thestandard transform defined by the commutative group of rotations in 2Dand translations in two- and three-dimensions, and will proceed to theFourier Transform of the non-commutative group of 3Drotations. Subjects covered will include correlation of images, shapematching, 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 exploremachine learning and perception algorithms focused on roboticapplications. Topics will include robot localization and mapping,pedestrian/obstacle detection/prediction, semantic segmentation,perception-based grasp planning, continual learning for perceptionalgorithms and multimodal sensor fusion. This course will includeintroductions to the topics by the instructor followed bypaper reading and discussions led by the students. In addition, thiscourse will consist of an in-depth semester long project that willemphasize research skills including developing a hypothesis,conducting literature reviews, formulating the problem, defining, andconducting 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: MULTILINGUALMETHODS Murray
This is a project based course focusing on the design andimplementation of systems that scale Natural Language Processingmethods beyond English. The course will cover both multilingual andcross-lingual methods with an emphasis on zero-shot and few-shotapproaches, as well as ‘silver’ dataset creation. Modules will includeCross-Lingual Information Extraction & Semantics, Cross-LanguageInformation Retrieval, Multilingual Question Answering, MultilingualStructured Prediction, Multilingual Automatic Speech Recognition, aswell as other non-English centric NLP methods. Students will beexpected to work in small groups and pick from one of the modules tocreate a model based on state-of-the-art methods covered in theclass. The course will be roughly two-thirds lecture based andone-third students presenting project updates periodically throughoutthe 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
CSCI-RSNG
ADVANCED TOPICS IN CAUSAL INFERENCE (3) Shpitser
This course will cover advanced topics on all areas of causalinference, including learning causal effects, path-specific effects,and optimal policies from data featuring biases induced by missingdata, confounders, selection, and measurement error, techniques forgeneralizing findings to different populations, complex probabilisticmodels relevant for causal inference applications, learning causalstructure from data, and inference under interference and networkeffects. The course will feature a final project which would involveeither an applied data analysis problem (with a causal inferenceflavor), 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 andmachine learning perspective. It covers techniques such as linear andnon-linear filtering, geometry, energy function methods, markov randomfields, conditional random fields, graphical models, probabilisticgrammars, and deep neural networks. These are illustrated on a set ofvision problems ranging from image segmentation, semanticsegmentation, depth estimation, object recognition, object parsing,scene parsing, action recognition, and text captioning. [Analysis orApplications]
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 65 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 topicswe will cover include but are not limit to different types of robustlearning methods, fair learning methods, safe learning methods, andresearch frontiers in transparency, interpretability, privacy,sustainability, AI safety and ethics. Students will learn thestate-of-the-art methods in lectures, understand the recent advancesby critiquing research articles, and apply/innovate new machinelearning methods in an application. There will be homework assignmentsand 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 SCIENCESEMINAR
Required for all CS PhD students. Strongly recommended for MSEstudents.
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 closelywith their assigned faculty supervisor. Successful completion of thiscourse 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 andinclusion in computer science (in particular) and engineering (ingeneral). We aim to study the ways in which the curriculum,environment, and structure of computer science within academiaperpetuates biases alienating female and minoritized students, and toexplore possible approaches for diversifying our field. The seminarwill meet on a weekly basis, readings will be assigned, and studentswill be expected to participate in the discussion.
Wed 3p 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 ofprogramming language design and implementation. topics covered include typetheory, process algebra, higher-order program analysis, and constraintsystems. 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, andapplications. It will also include selected topics relating to medical imageacquisition, especially where they relate to analysis. The purpose of thecourse is to provide the participants with a thorough background in currentresearch in these areas, as well as to promote greater awareness andinteraction between multiple research groups within the University. Theformat of the course is informal. Students will read selected papers. Allstudents will be assumed to have read these papers by the time the paper isscheduled for discussion. But individual students will be assigned on arotating basis to lead the discussion on particular papers or sections ofpapers. 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 willlead 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 inrecent years on English NLP tasks, many methods fail on otherlanguages due to both linguistic differences as well as lack ofavailable annotated resources. This course will focus onCross-Language Information Retrieval, Cross-Lingual InformationExtraction, Multilingual Semantics, Massively Multilingual LanguageModeling, and other non-English NLP sub-fields. Students will beexpected 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 fieldand potentially relevant material from related fields. In addition toreading and discussing each week's paper, enrolledstudents are expected to take turns selecting papers and leading the discussion.
Required course background: EN.601.465/665 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 oncomputational 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 currentresearch 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-guidedintervention. Sensor based robotics, including computer vision andbiomedical image analysis. Algorithmic robotics, robot control andmachine learning. Autonomous robotics for monitoring, exploration andmanipulation with applications in home, environmental (land, sea,space), and defense areas. Biorobotics and neuromechanics, includingdevices, algorithms and approaches to robotics inspired by principlesin biomechanics and neuroscience. Human-machine systems, includinghaptic and visual feedback, human perception, cognition and decisionmaking, and human-machine collaborative systems. Cross-listed with Mechanical Engineering, Computer Science, Electrical and ComputerEngineering, and Biomedical Engineering.
Wed 12-1:30
limit 80
520.702
CURRENT TOPICS IN LANGUAGE AND SPEECHPROCESSING Trmal
CLSP seminar series, for any students interested in current topics inlanguage and speech processing.
Mon & Fri 12-1:15
580.458/658
COMPUTING THE TRANSCRIPTOME (3) Pertea
The primary goal of this course is for students to learn the leading computational tools used in the field of transcriptomics, as well as the theory concepts behind them, in order to be able to analyze the genes and transcripts expressed in a living cell. Lectures will cover different practical ways to analyze large data sets generated by high-throughput RNA sequencing (RNA-Seq) experiments, including alignment, assembly, and quantification. You will learn about different technologies of RNA-seq and how they influence the transcriptome you are computing, what are the best practices for RNA-seq data analysis, what are the methods for transcriptome assembly and quantification, how do you measure the transcript expression levels, how do you find which genes are differentially expressed between different RNA-seq datasets, and how do you visualize your results.
Prereq: knowledge of the Unix operating system and programming expertise in a language such as Perl or Python. Familiarity with R recommended.
TuTh 4:30-5:45p
limit 10 + 15
650.624
CSCI-SYST
NETWORK SECURITY Johnston
This course focuses on communication security in computer systems andnetworks. The course is intended to provide students with anintroduction to the field of network security. The course coversnetwork security services such as authentication and access control,integrity and confidentiality of data, firewalls and relatedtechnologies, Web security and privacy. Course work involvesimplementing various security techniques. A course project isrequired.
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 thatgather and analyze security events to bring situational awareness andenable IT staff to understand and analyze events that pose thegreatest risk. Increasingly, detecting and preventing cyber attacksrequire sophisticated use of data analytics and machine learningtools. This course will cover fundamental theories and methods in datascience, modern security analytical tools, and practical use cases ofsecurity analytics. Students of this course learn concepts, tasks, andmethods of data science; and how to apply data science to cybersecurity problems. Students also learn how to use modern software insecurity 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
