Fall 2021 Courses

500.112 (E)

GATEWAY COMPUTING: JAVA (3) More, Simari

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.

Sections start on the hour, from 8a - 4p. Sections 2 & 8 (9a & 3p) are restricted to incoming CS majors.

MWF 50 minutes, limit 19/section
in person

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.

Sec 01: Wed 4:30-6:30p, alternate weeks starting 9/1
Sec 02: Wed 4:30-6:30p, alternate weeks starting 9/8
Sec 03: Tue 4:30-6:30p, alternate weeks starting 9/7
limit 19 each, CS majors only
in person

601.105

CS FIRST-YEAR EXPERIENCE (1) staff

This course provides first-year computer science majors with an introduction to the field and department. A variety of faculty members will lead weekly small group discussion sections on topics of interest related to the discipline. Upper-year majors will serve as peer mentors for each group (enrollment by permission only). Satisfactory/Unsatisfactory only; counts as elective credits only, not towards CS course credit requirement.

Sec 01: Burns, Mon 6-6:50p
Sec 02: C-M Huang, Tue 8-8:50a
Sec 03: Smith, Wed 9-9:50a
Sec 04: Dinitz, Wed 4:30-5:20p
Sec 05: Cao, Tue 1:30-2:20p
Sec 06: Lippincott, Mon 4:30-5:20p
Sec 07: Eisner, Thu 9-9:50a
limit 9 first-year majors + 2 upper-year majors
in person

601.220 (E)

INTERMEDIATE PROGRAMMING (4) Darvish, Selinski, Simari

This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover low-level programming techniques, as well as object-oriented class design, and the use of class libraries. Specific topics include pointers, dynamic memory allocation, polymorphism, overloading, inheritance, templates, collections, exceptions, and others as time permits. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages.

Prereq: AP CS or (>=C+ grade in one of 601.107, 500.112, 500.113, 500.114, 580.200) or (500.132 or 500.133 or 500.134) or equivalent by permission.

CS/CE majors/minors only
Sec 01 (Simari): MWF 12:00-1:15pm
Sec 03 (Selinski): MWF 3:00-4:15pm (incoming CS/CE first-years only)
Sec 04 (Darvish): MWF 10-11:15am
Sec 05 (Darvish): MWF 8:30-9:45am (incoming CS/CE first-years only) limit 35/section
in person

601.226 (EQ)

DATA STRUCTURES (4) Madooei

This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments.

Prereq: AP CS or >= C+ grade in 601.107, 601.220, 500.112, 500.113+500.132, 500.114+500.132 or equivalent by permission.

Sec 01: MWF 12-1:15pm, limit 75
Sec 02: MWF 1:30-2:45pm, limit 75

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.

01: MWF 9-9:50am, limit 49, in person
02: MWF 10-10:50am, limit 75

601.231 (EQ)

AUTOMATA and COMPUTATION THEORY (3) More

This course is an introduction to the theory of computing. topics include design of finite state automata, pushdown automata, linear bounded automata, Turing machines and phrase structure grammars; correspondence between automata and grammars; computable functions, decidable and undecidable problems, P and NP problems, NP-completeness, and randomization. Students may not receive credit for 601.231 and 601.631 for the same degree.

Prereq: 550/553.171/172.

Sec 01: Tu 9-10:15am, Th 9-10:20am
Sec 02: Tu 9-10:15am, Th 9-10:20am
Sec 03: Tu 9-10:15am, Th 9-10:20am
Sec 04: Tu 9-10:20am, Th 9-10:15am
Sec 05: Tu 9-10:20am, Th 9-10:15am
Sec 06: Tu 9-10:20am, Th 9-10:15am
limit 15/section

601.270 (E)
NEW COURSE!!

OPEN SOURCE SOFTWARE ENGINEERING (Semesters of Code I) (3) Walli

The course will provide students a development experience focused on learning software engineering skills to deliver software at scale to a broad community of users associated with open source licensed projects. The class work will introduce students to ideas behind open source software with structured modules on recognizing and building healthy project structure, intellectual property basics, community & project governance, social and ethical concerns, and software economics.
The practical side of the course will engage and mentor students directly in OSI-licensed project communities to provide hands-on learning experiences of practices covered in the classroom modules, and team building experience working in the project.

Prereq: 601.220 and 601.226.

TuTh 10:30-11:45am
limit 28
in person

601.280 (E)

FULL-STACK JAVASCRIPT (3) Madooei

A full-stack JavaScript developer is a person who can build modern software applications using primarily the JavaScript programming language. Creating a modern software application involves integrating many technologies - from creating the user interface to saving information in a database and everything else in between and beyond. A full-stack developer is not an expert in everything. Rather, they are someone who is familiar with various (software application) frameworks and the ability to take a concept and turn it into a finished product. This course will teach you programming in JavaScript and introduce you to several JavaScript frameworks that would enable you to build modern web, cross-platform desktop, and native/hybrid mobile applications. A student who successfully completes this course will be on the expedited path to becoming a full-stack JavaScript developer.

Prereq: 601.220 or 601.226. Students must not have taken or be concurrently enrolled in 601.421/621 OOSE.

TuTh 12-1:15pm
limit 65

601.315 (E)
CSCI-SOFT

DATABASES (3) Yarowsky

Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html)

Prereq: 601.226. Students may receive credit for only one of 601.315/415/615.

TuTh 3-4:15pm
limit 30

601.318 (E)
CSCI-SYST

OPERATING SYSTEMS (3) Huang

This course covers the fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system. [Systems]

Prereq: 601.220 & 601.226 & 601.229. Students may receive credit for only one of 601.318/418/618.

TuTh 1:30-2:45pm
limit 15
in person

601.320 (E)
CSCI-SOFT CSCI-SYST

PARALLEL PROGRAMMING (3) Burns

This course prepares the programmer to tackle the massive data sets and huge problem size of modern scientific and enterprise computing. Google and IBM have commented that undergraduate CS majors are unable to "break the single server mindset" (http://www.google.com/intl/en/ press/pressrel/20071008_ibm_univ.html). Students taking this course will abandon the comfort of serial algorithmic thinking and learn to harness the power of cutting-edge software and hardware technologies. The issue of parallelism spans many architectural levels. Even ``single server'' systems must parallelize computation in order to exploit the inherent parallelism of recent multi-core processors. The course will examine different forms of parallelism in four sections. These are: (1) massive data-parallel computations with Hadoop!; (2) programming compute clusters with MPI; (3) thread-level parallelism in Java; and, (4) GPGPU parallel programming with NVIDIA's Cuda. Each section will be approximately 3 weeks and each section will involve a programming project. The course is also suitable for undergraduate and graduate students from other science and engineering disciplines that have prior programming experience. [Systems]

Prereq: 601.226 and 601.229. Students may receive credit for at most one of 601.320/420/620.

MW 4:30-5:45pm
limit 25

601.340 (E)
CSCI-SOFT CSCI-SYST

WEB SECURITY (3) Cao

This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems]

Note: This undergrad version will not have the same paper component as the other versions of this course. Prerequisite: 601.226 & 601.229. Recommended: 601.280. Students may receive credit for only one of 601.340/440/640.

TuTh 12-1:15
limit 10
in person

601.415 (E)
CSCI-SOFT

DATABASES (3) Yarowsky

Similar material as 601.315, covered in more depth, for advanced undergraduates. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html)

Prereq: 601.226. Students may receive credit for only one of 601.315/415/615.

TuTh 3-4:15pm
limit 18

601.417 (E)
CSCI-SYST

DISTRIBUTED SYSTEMS (3) Amir

The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive hands-on experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester (odd years) and is a good introduction course to the 601.717 Advanced Distributed Systems and Networks project-focused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses)

Prereq: 601.220 & 601.226. Students may receive credit for only one of 601.417/617.

TuTh 3-4:15pm
limit 19
in person

601.418 (E)
CSCI-SYST

OPERATING SYSTEMS (3) Huang

Similar material as 601.318, covered in more depth, for advanced undergraduates. [Systems]

Prereq: 601.220 & 601.226 & 601.229. Students may receive credit for only one of 601.318/418/618.

TuTh 1:30-2:45pm
limit 15
in person

601.420 (E)
CSCI-SOFT CSCI-SYST

PARALLEL PROGRAMMING (3) Burns

More advanced version of 601.320. Students may receive credit for at most one of 601.320/420/620. [Systems]

Required course background: 601.226 and 601.229 or equiv.

MW 4:30-5:45pm
limit 25

601.421 (E)
CSCI-SOFT, CSCI-TEAM

OBJECT ORIENTED SOFTWARE ENGINEERING (3) Darvish

This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. [(Systems or Applications), Oral] (https://www.jhu-oose.com)

Prereq: 600/601.226 & 600.120/601.220. Strongly recommended: EN.601.280 or EN.601.290 (will be requirement in future semesters). Students may receive credit for only one of 601.421/621.

Lec: Tu 1:30-2:45pm
Sec 01: Th 1:30-2:45pm
Sec 02: Th 1:30-2:45pm
Sec 03: Th 1:30-2:45pm
Sec 04: Th 1:30-2:45pm
limit 19/section
sections will meet in person

601.428 (E)
CSCI-SOFT

COMPILERS & INTERPRETERS (3) Hovemeyer

Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. [Systems]>

Prereq: 601.220 & 601.226 & 601.229; 601.231 recommended

MW 12-1:15pm
limit 24
in person

601.429 (E)
CSCI-SOFT

FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith

How can we effectively use functional programming techniques to build real-world software? This course will primarily focus on using the OCaml programming language for this purpose. Topics covered include OCaml basics, modules, standard libraries, testing, quickcheck, build tools, functional data structures and efficiency analysis, monads, streams, and promises. Students will practice what they learn in lecture via functional programming assignments and a final project. Pre-req: 601.226 or instructor permission.

MW 1:30-2:45pm
limit 34
in person

601.430 (EQ)
CSCI-THRY

COMBINATORICS AND GRAPH THEORY IN CS Li

This course covers the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis]

Pre-requisite: 550.171/553.171/553.172; probability theory and linear algebra recommended. Students may receive credit for only one of EN.601.430 and EN.601.630.

TuTh 1:30-2:45pm
limit 10
in person

601.433 (EQ)
CSCI-THRY

INTRO ALGORITHMS (3) Dinitz

This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as 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 & (550/553.171/172 or 601.231) or Perm. Required. Students may receive credit for only one of 601.433/633.

TuTh 12-1:15pm
limit 75

601.437 (EQ)
CSCI-RSNG

NEW COURSE

FEDERATED LEARNING & ANALYTICS (3) Braverman

Federated Learning (FL) is an area of machine learning where data is distributed across multiple devices and training is performed without exchanging the data between devices. FL can be contrasted with classical machine learning settings when data is available in a central location. As such, FL faces additional challenges and limitations such as privacy and communication. For example, FL may deal with questions of learning from sensitive data on mobile devices while protecting privacy of individual users and dealing with low power and limited communication. As a result, FL requires knowledge of many interdisciplinary areas such as differential privacy, distributed optimization, sketching algorithms, compression and more. In this course students will learn basic concepts and algorithms for FL and federated analytics, and gain hands-on experience with new methods and techniques. Students will gain understanding in reasoning about possible trade-offs between privacy, accuracy and communication. [Analysis]

Prereq: 601.433/633, prob/stat, and (601.464/664 or 601.475/675). Students may receive credit for only one of 601.437/637.

TuTh 12-1:15pm
limit 15

601.440 (E)
CSCI-SOFT CSCI-SYST

WEB SECURITY (3) Cao

This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems]

Prerequisite: 601.226 & 601.229. Recommended: 601.280. Students may receive credit for only one of 601.340/440/640.

TuTh 12-1:15pm
limit 9
in person

601.443 (E)
CSCI-SOFT

SECURITY AND PRIVACY IN COMPUTING (3) Rubin

Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semester-long project that will be done in teams and will include a presentation by each group to the class. [Applications]

Prerequisite: 601.229 & (601.318/418 or 601.414) Students may receive credit for only one of 601.443/643.

Tu 9-10:10, Th 9-10:20
limit 19
in person

601.445 (E)
CSCI-SOFT

PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green & Chator

This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem. We will place a particular emphasis on the techniques of provable security and the feasibility of reverse-engineering undocumented cryptographic systems. [Systems]

Prereq: 601.226 & 601.229. Students may receive credit for only one of 601.445/645.

MW 12-1:15
limit 25
in person

601.447 (E)
CSCI-APPL, CSCI-TEAM

COMPUTATIONAL GENOMICS: SEQUENCES (3) Langmead

Your genome is the blueprint for the molecules in your body. It's also a string of letters (A, C, G and T) about 3 billion letters long. How does this string give rise to you? Your heart, your brain, your health? This, broadly speaking, is what genomics research is about. This course will familiarize you with a breadth of topics from the field of computational genomics. The emphasis is on current research problems, real-world genomics data, and efficient software implementations for analyzing data. Topics will include: string matching, sequence alignment and indexing, assembly, and sequence models. Course will involve significant programming nprojects. [Applications, Oral]

Prereq: 601.220 & 601.226. Students may receive credit for at most one of 601.447/647/747.

TuTh 9-10:15am
limit 24
in person

601.452 (E)
(no tag)

COMPUTATIONAL BIOMEDICAL RESEARCH (3) Schatz

[Co-listed with AS.020.415] This course for advanced undergraduates includes classroom instruction in interdisciplinary research approaches and lab work on an independent research project in the lab of a Bloomberg Distinguished Professor and other distinguished faculty. Lectures will focus on cross-cutting techniques such as data visualization, statistical inference, and scientific computing. In addition to two 50-minute classes per week, students will commit to working approximately 3 hours per week in the lab of one of the professors. The student and professor will work together to schedule the research project. Students will present their work at a symposium at the end of the semester. Prereq: permission required.

MW 3-3:50pm
limit 15
in person

601.455 (E)
CSCI-APPL

COMPUTER INTEGRATED SURGERY I (4) Taylor

This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. [Applications] (http://www.cisst.org/~cista/445/index.html)

Prereq: 601.226 and linear algebra, or permission. Recmd: 601.220, 601.457, 601.461, image processing. Students may earn credit for only one of 601.455/655.

TuTh 1:30-2:45pm
limit 29
in person

601.457 (EQ)
CSCI-APPL

COMPUTER GRAPHICS (3) Kazhdan

This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. [Applications]

Prereq: no audits; 601.220 & 601.226 & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.

MWF 11
limit 29
in person

601.461 (EQ)
CSCI-APPL

COMPUTER VISION (3) Leonard

This course provides an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3­D geometry from binocular stereo, motion, and photometric stereo, and object recognition, image segmentation, and activity analysis. Elements of machine vision and biological vision are also included. [Applications]

Prereq: intro programming, linear algebra, prob/stat. Students can earn credit for at most one of 601.461/661/761.

TuTh 12-1:15
limit 39 hybrid mode

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 & probability. Students may receive credit for only one of 601.463/663/763.

TuTh 4:30-5:45
limit 10
in person

601.464 (E)
CSCI-RSNG

ARTIFICIAL INTELLIGENCE (3) Mielke

The class is recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as learning, planning and prediction. Materials will be primarily based on the popular textbook, Artificial Intelligence: A Modern Approach. Strong programming skills are expected, as well as basic familiarity with probability. For students intending to also take courses in Machine Learning (e.g., 601.475/675, 601.476/676), they may find it beneficial to take this course first, or concurrently. [Applications]

Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664

MW 3-4:15
limit 35

601.465 (E)
CSCI-APPL

NATURAL LANGUAGE PROCESSING (4) Eisner

This course is an in-depth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers? The course covers methods for trees (parsing and semantic interpretation), sequences (finite-state transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech tagging, speech recognition and machine translation. There are a number of structured but challenging programming assignments. [Applications] (www.cs.jhu.edu/~jason/465)

Prerequisite: 601.226 and basic familiarity with Python, partial derivatives, matrix multiplication, and probabilities. Students may receive credit for at most one of 601.465/665.

MWF 3-4:15
sec Tu 6-7:30p
limit 23
in person

601.467 (E)
CSCI-APPL

INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn

This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. [Applications]

Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667.

TuTh 9-10:15
limit 20

601.468 (E)
CSCI-APPL

MACHINE TRANSLATION (3) Koehn

Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. [Applications]

Required course background: prob/stat, 601.226. Student may receive credit for at most one of 601.468/668.

TuTh 1:30-2:45
limit 18
in person

EN.601.474 (EQ)
CSCI-THRY

ML: LEARNING THEORY (3) Arora

This is a graduate level course in machine learning. It will provide a formal and in-depth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine- learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. [Analysis]

Pre-reqs: multivariable calculus (110.202 or 110.211) & probability (553.310/553.311 or 553.420 or 560.348) & linear algebra (110.201 or 110.212 or 553.291) & intro computing (EN.500.112, EN.500.113, EN.500.114, EN.601.220 or AS.250.205). Recommended: prior coursework in ML. Students may receive credit for only one of 601.474/674.

TuTh 9-10:15a
limit 25

601.475 (E)
CSCI-RSNG

MACHINE LEARNING (3) Shpitser

Machine learning is subfield of computer science and artificial intelligence, whose goal is to develop computational systems, methods, and algorithms that can learn from data to improve their performance. This course introduces the foundational concepts of modern Machine Learning, including core principles, popular algorithms and modeling platforms. This will include both supervised learning, which includes popular algorithms like SVMs, logistic regression, boosting and deep learning, as well as unsupervised learning frameworks, which include Expectation Maximization and graphical models. Homework assignments include a heavy programming components, requiring students to implement several machine learning algorithms in a common learning framework. Additionally, analytical homework questions will explore various machine learning concepts, building on the pre-requisites that include probability, linear algebra, multi-variate calculus and basic optimization. Students in the course will develop a learning system for a final project. [Applications or Analysis]

Pre-reqs: multivariable calculus (110.202 or 110.211) & probability (553.310/553.311 or 553.420 or 560.348) & linear algebra (110.201 or 110.212 or 553.291) & intro computing (EN.500.112, EN.500.113, EN.500.114, EN.601.220 or AS.250.205). Students may receive credit for only one of 601.475/675.

MWF 1:30-2:45
limit 45

601.477 (EQ)
CSCI-RSNG

CAUSAL INFERENCE (3) Shpitser

"Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. [Analysis]

Pre-requisites: familiarity with R programming and (601.475/675 or stats/probability) or permission. Students may receive credit for at most one of 601.477/677.

TuTh 3-4:15
limit 10
in person

AS.050.375 (Q)
CSCI-APPL

PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille

[Was EN.601.485, now cross-listed as AS.050.375] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. [Applications or Analysis]

Pre-requisites: Calc I, programming experience (Python preferred).

TuTh 9-10:15
limit 25
in person

601.490 (E)
CSCI-SOFT, CSCI-TEAM

INTRO TO HUMAN-COMPUTER INTERACTION (3) C-M Huang

This course is designed to introduce undergraduate and graduate students to design techniques and practices in human-computer interaction (HCI), the study of interactions between humans and computing systems. Students will learn design techniques and evaluation methods, as well as current practices and exploratory approaches, in HCI through lectures, readings, and assignments. Students will practice various design techniques and evaluation methods through hands-on projects focusing on different computing technologies and application domains. This course is intended for undergraduate and graduate students in Computer Science/Cognitive Science/Psychology. Interested students from different disciplines should contact the instructor before enrolling in this course. [Applications]

Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both.

TuTh 3-4:15
Sec 01: CS majors/minors, limit 30
Sec 02: KSAS students, limit 10
in person

601.501

COMPUTER SCIENCE WORKSHOP

An 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.

Perm. of faculty supervisor req'd.

See below for faculty section numbers

601.503

INDEPENDENT STUDY

Individual, guided study for undergraduate students under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved.

Permission required.

See below for faculty section numbers

601.507

UNDERGRADUATE RESEARCH

Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved.

Permission required.

See below for faculty section numbers and whether to select 507 or 517.

601.509

COMPUTER SCIENCE INTERNSHIP

Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study and credit assigned must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit. S/U only.

Permission required.

See below for faculty section numbers

601.517

GROUP UNDERGRADUATE RESEARCH

Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved.

Permission required.

Only for faculty specifically marked below.

601.519

SENIOR HONORS THESIS (3)

For computer science majors only. The student will undertake a substantial independent research project under the supervision of a faculty member, potentially leading to the notation "Departmental Honors with Thesis" on the final transcript. Students are expected to enroll in both semesters of this course during their senior year. Project proposals must be submitted and accepted in the preceding spring semester (junior year) before registration. Students will present their work publically before April 1st of senior year. They will also submit a first draft of their project report (thesis document) at that time. Faculty will meet to decide if the thesis will be accepted for honors.

Prereq: 3.5 GPA in Computer Science after spring of junior year and permission of faculty supervisor.

See below for faculty section numbers

601.556

SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3)

The student will undertake a substantial independent research project in the area of computer-integrated surgery, under joint supervision of a WSE faculty adviser and a clinician or clinical researcher at the Johns Hopkins Medical School.

Prereq: 601.455 or perm req'd.

Section 1: Taylor

601.615
CSCI-SOFT

DATABASES Yarowsky

Same material as 601.415, for graduate students. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html)

Required course background: Data Structures. Students may receive credit for only one of 601.315/415/615.

TuTh 3-4:15
limit 44

601.617
CSCI-SYST

DISTRIBUTED SYSTEMS (3) Amir

The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive hands-on experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester (odd years) and is a good introduction course to the 601.717 Advanced Distributed Systems and Networks project-focused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses)

Prereq: Intermediate Programming (C/C++) and Data Structures. Students may receive credit for only one of 601.417/617.

TuTh 3-4:15
limit 20
in person

601.618
CSCI-SYST

OPERATING SYSTEMS Huang

Same material as 601.418, for graduate students. [Systems]

Required course background: Data Structures & Computer System Fundamentals. Students may receive credit for only one of 601.318/418/618.

TuTh 1:30-2:45
limit 15
in person

601.620
CSCI-SOFT CSCI-SYST

PARALLEL PROGRAMMING (3) Burns

Same as 601.420, for graduate students. Students may receive credit for at most one of 601.320/420/620. [Systems]

Required course background: 601.226 and 601.229 or equiv.

MW 4:30-5:45
limit 40

601.621
CSCI-SOFT

OBJECT ORIENTED SOFTWARE ENGINEERING Darvish

Same material as 601.421, for graduate students. [Systems or Applications] (https://www.jhu-oose.com)

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.

TuTh 1:30-2:45
limit 25

601.628
CSCI-SOFT

COMPILERS & INTERPRETERS Hovemeyer

Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. [Systems]

Prereq: 601.220, 601.226 & 601.229; 601.231 recommended

MW 12-1:15
limit 24
in person

601.629
CSCI-SOFT
NEW COURSE

FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith

How can we effectively use functional programming techniques to build real-world software? This course will primarily focus on using the OCaml programming language for this purpose. Topics covered include OCaml basics, modules, standard libraries, testing, quickcheck, build tools, functional data structures and efficiency analysis, monads, streams, and promises. Students will practice what they learn in lecture via functional programming assignments and a final project. Required Background: data structures.

MW 1:30-2:45pm
limit 15
in person

601.630
CSCI-THRY

COMBINATORICS AND GRAPH THEORY IN CS Li

This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis]

Required Background: discrete math; probability theory and linear algebra recommended. Student may receive credit for only one of 601.430/601.630.

TuTh 1:30-2:45p
limit 20
in person

601.633
CSCI-THRY

INTRO ALGORITHMS Dinitz

Same material as 601.433, for graduate students. [Analysis]

Required Background: Data Structures and (Discrete Math or Automata/Computation Theory). Students may receive credit for only one of 601.433/633.

TuTh 12-1:15
limit 75

601.637
CSCI-RSNG

NEW COURSE

FEDERATED LEARNING & ANALYTICS (3) Braverman

Federated Learning (FL) is an area of machine learning where data is distributed across multiple devices and training is performed without exchanging the data between devices. FL can be contrasted with classical machine learning settings when data is available in a central location. As such, FL faces additional challenges and limitations such as privacy and communication. For example, FL may deal with questions of learning from sensitive data on mobile devices while protecting privacy of individual users and dealing with low power and limited communication. As a result, FL requires knowledge of many interdisciplinary areas such as differential privacy, distributed optimization, sketching algorithms, compression and more. In this course students will learn basic concepts and algorithms for FL and federated analytics, and gain hands-on experience with new methods and techniques. Students will gain understanding in reasoning about possible trade-offs between privacy, accuracy and communication. [Analysis]

Required course background: 601.433/633, prob/stat, and (601.464/664 or 601.475/675). Students may receive credit for only one of 601.437/637.

TuTh 12-1:15pm
limit 30

601.640
CSCI-SOFT CSCI-SYST

WEB SECURITY (3) Cao

This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. [Systems]

Required course background: data structures and computer system fundamentals. Students may receive credit for only one of 601.340/440/640.

TuTh 12-1:15
limit 20
in person

601.643
CSCI-SOFT

SECURITY AND PRIVACY IN COMPUTING Rubin

Same material as 601.443, for graduate students. [Applications]

Required course background: A basic course in operating systems and networking, or permission of instructor.

Tu 9-10:10, Th 9-10:20
limit 19
in person

601.645
CSCI-SOFT

PRACTICAL CRYPTOGRAPHIC SYSTEMS Green & Chator

Same material as 601.445, for graduate students. [Systems]

Prereqs: data structures & computer system fundamentals. Students may receive credit for only one of 601.445/645.

MW 12-1:15
limit 30
in person

601.647
CSCI-APPL

COMPUTATIONAL GENOMICS: SEQUENCES Langmead

Same material as 601.447, for graduate students. [Applications]

Required Course Background: Intermediate Programming (C/C++) and Data Structures. Students may earn credit for at most one of 601.447/647/747.

TuTh 9-10:15
limit 24
in person

601.655
CSCI-APPL

COMPUTER INTEGRATED SURGERY I Taylor

Same material as 601.455, for graduate students. [Applications] (http://www.cisst.org/~cista/445/index.html)

Prereq: data structures and linear algebra, or permission. Recommended: intermediate programming in C/C++, computer graphics, computer vision, image processing. Students may earn credit for 601.455 or 601.655, but not both.

TuTh 1:30-2:45
limit 20
in person

601.657
CSCI-APPL

COMPUTER GRAPHICS Kazhdan

Same material as 601.457, for graduate students.

Prereq: no audits; Intermediate Programming (C/C++) & Data Structures & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.

MWF 1:30
limit 20
in person

601.661
CSCI-APPL

COMPUTER VISION Leonard

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 12-1:15
limit 49 hybrid mode

601.663
CSCI-APPL

ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard

Same material as EN.601.463, for graduate students. [Analysis]

Required course background: data structures & linear algebra & prob/stat. Students may receive credit for only one of 601.463/663/763.

TuTh 4:30-5:45
limit 35
in person

601.664
CSCI-RSNG

ARTIFICIAL INTELLIGENCE Mielke

Same as 601.464, for graduate students. [Applications]

Prereq: Data Structures; Recommended: linear algebra & prob/stat. Students can only receive credit for one of 601.464/664

MW 3-4:15p
limit 35

601.665
CSCI-APPL

NATURAL LANGUAGE PROCESSING Eisner

Same material as 601.465, for graduate students. [Applications] (www.cs.jhu.edu/~jason/465)

Prerequisite: data structures and basic familiarity with Python, partial derivatives, matrix multiplication, and probabilities. Students may receive credit for at most one of 601.465/665.

MWF 3-4:15
sec Tu 6-7:30p
limit 26
in person

601.667 (E)
CSCI-APPL

INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn

This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. [Applications]

Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667.

TuTh 9-10:15
limit 29

601.668
CSCI-APPL

MACHINE TRANSLATION Koehn

Same material as 601.468, for graduate students. [Applications]

Required course background: prob/stat, data structures. Student may receive credit for at most one of 601.468/668.

TuTh 1:30-2:45
limit 30
in person

EN.601.674
CSCI-THRY

ML: LEARNING THEORY Arora

[Formerly: Statistical Machine Learning] This is a graduate level course in machine learning. It will provide a formal and in-depth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine- learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. [Analysis]

Required course background: multivariable calculus, probability, linear algebra, intro computing. Recommended: prior coursework in ML. Students may receive credit for only one of 601.474/674.

TuTh 9-10:15a
limit 30

601.675
CSCI-RSNG

MACHINE LEARNING Shpitser

Same material as 601.475, for graduate students. [Applications or Analysis]

Required course background: multivariable calculus, probability, linear algebra, intro computing. Student may receive credit for only one of 601.475/675.

MWF 1:30-2:45
limit 65

601.677
CSCI-RSNG

CAUSAL INFERENCE Shpitser

Same material as 601.477, for graduate students. [Analysis]

Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Students may receive credit for at most one of 601.477/677.

TuTh 3-4:15
limit 19
in person

AS.050.675
CSCI-APPL

PROBABILISTIC MODELS OF THE VISUAL CORTEX Yuille

[Was EN.601.685, now cross-listed as AS.050.675.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. [Applications or Analysis]

Pre-requisites: Calc I, programming experience (Python preferred).

TuTh 9-10:15
limit 10
in person

601.690
CSCI-SOFT

INTRO TO HUMAN-COMPUTER INTERACTION C-M Huang

Same material as EN.601.490, for graduate students. [Applications]

Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both.

TuTh 3-4:15
limit 20
in person

601.714
CSCI-SYST

ADVANCED COMPUTER NETWORKS Ghorbani

This is a graduate-level course on computer networks. It provides a comprehensive overview on advanced topics in network protocols and networked systems. The course will cover both classic papers on Internet protocols and recent research results. It will examine a wide range of topics, e.g., routing, congestion control, network architectures, datacenter networks, network virtualization, software-defined networking, and programmable networks, with an emphasize on core networking concepts and principles. The course will include lectures, paper discussions, programming assignments and a research project. [Systems]
Recommended Course Background: One undergraduate course in computer networks (e.g., EN.601.414/614 Computer Network Fundamentals or the equivalent), or permission of the instructor. The course assignments and projects assume students to be comfortable with programming.

Pre-req: EN.601.414/614 or equivalent.

TuTh 4:30-5:45pm
limit 25
in person

601.801

COMPUTER SCIENCE SEMINAR

Attendance recommended for all grad students; only 1st & 2nd year PhD students may register.

TuTh 10:30-12
limit 90

601.803

MASTERS RESEARCH

Independent research for masters students. Permission required.

See below for faculty section numbers.

601.805

GRADUATE INDEPENDENT STUDY

Permission required.

See below for faculty section numbers.

601.807

TEACHING PRACTICUM Smith

PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement. Permission required.

limit 25

601.809

PHD RESEARCH

Independent research for PhD students.

See below for faculty section numbers.

AS.050.814

RESEARCH SEMINAR IN COMPUTER VISION Yuille

This course covers advanced topics in computational vision. It discusses and reviews recent progress and technical advances in visual topics such as object recognition, scene understanding, and image parsing.

tba

601.817

SELECTED TOPICS IN SYSTEMS RESEARCH R.Huang

This course covers latest advances in the research of computer systems including operating systems, distributed system, mobile and cloud computing. Students will read and discuss recent research papers in top systems conferences. Each week, one student will present the paper and lead the discussion for the week. The focus topics covered in the papers vary semester to semester. Example topics include fault-tolerance, reliability, verification, energy efficiency, and virtualization.

Fr 1:30-2:45
limit 14
in person

601.826

SELECTED TOPICS IN PROGRAMMING LANGUAGES Smith

This course covers recent developments in the foundations of programming language design and implementation. topics covered vary from year to year. Students will present papers orally.

Fr 11-12
limit 15
in person

601.831

CS THEORY SEMINAR Dinitz, Li

Seminar series in theoretical computer science. Topics include algorithms, complexity theory, and related areas of TCS. Speakers will be a mix of internal and external researchers, mostly presenting recently published research papers.

W 12
limit 30
in person

601.845

SELECTED TOPICS IN APPLIED CRYPTOGRAPHY Green

In this course students will read, discuss and present current research papers in applied cryptography. Topic coverage will vary each semester.

Prereq: permission of instructor.

Tu 12-12:50
limit 12
in person

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.

W 4p
limit 15
in person

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.

W 12-1:15p
limit 15
in person

601.866

SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme

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

Fr 10:45-11:45
limit 15
in person

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-noon
limit 15
in person

520.702

CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING staff

CLSP seminar series, for any students interested in current topics in language and speech processing.

Tu & Fr 12-1:15

500.745

SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, 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

650.601

INTRODUCTION TO INFORMATION SECURITY Xiangyang Li

This course exposes students to the cross-disciplinary and broad information security field. It surveys a range of fundamental topics of information security principles, architecture, policy and standard, risk management, cryptography, physical, operation, system and network security mechanisms, and law and ethics, among others. This course includes lectures, case studies, and homework. Students will also complete independent study class projects. Recommended Course Background: Basic knowledge of computer system and information technology.

TuTh 12-1:15
limit 25
in person

650.614

RIGHTS IN THE DIGITAL AGE Michael Jacobs

This course will examine various legal and policy issues presented by the tremendous growth in computer technology, especially the Internet. The rights that various parties have with respect to creating, modifying, using, distributing, storing, and copying digital data will be explored. The concurrent responsibilities, and potential liabilities, of those parties will also be addressed. The course will focus on intellectual property issues, especially copyright law, and other legal and economic considerations related to the use and management of digital data. Copyright law and its role within the framework of intellectual property law will be presented in a historical context with an emphasis on its applicability to emerging-technology issues. Specifically, the treatment of various works, such as music, film, and photography that were traditionally, analog in nature will be analyzed with respect to their treatment in the digital domain; works that are by their nature digital, such as computer software, will also be analyzed. The current state of U.S. copyright law will be presented, as will relevant international treaties and foreign laws. The goal of the course is to provide those involved or interested in digital rights management with a general awareness of the rights and obligations associated with maintaining and distributing digital data. (This course will be taught in Washington, DC and video-cast on Homewood Campus.)

W 4:30-6:30p
limit 25
in person

650.621

CRITICAL INFRASTRUCTURE PROTECTION Lanier Watkins

This course focuses on understanding the history, the vulnerability, and the need to protect our Critical Infrastructure and Key Resources (CIKR). We will start by briefly surveying the policies which define the issues surrounding CIKR and the strategies that have been identified to protect them. Most importantly, we will take a comprehensive approach to evaluating the technical vulnerabilities of the 18 identified sectors, and we will discuss the tactics that are necessary to mitigate the risks associated with each sector. These vulnerabilities will be discussed from the perspective of ACM, IEEE or other technical journals/articles which detail recent and relevant network-level CIKR exploits. We will cover well known vulnerable systems such the Internet, SCADA or PLC and lesser known systems such as E911 and industrial robot. Also, a class project is required. Recommended Course Background: EN.650.424 or equivalent or permission by instructor.

Th 4:30-7p
limit 30
in person

650.655

IMPLEMENTING EFFECTIVE INFORMATION SECURITY PROJECTS Michael Kociemba

This course focuses on the personnel, legal, regulatory and privacy issues that comprise the basic security management areas that must be considered when developing and implementing an effective information security program. Specific topics include security-related legislation, government and industry security frameworks, the identification and management of risk, security controls, defense in depth, critical infrastructure protection, development and implementation of an enterprise wide security strategy, and organizational roles and responsibilities.

Tu 6-8:30p
limit 30
in person

650.656

COMPUTER FORENSICS Timothy Leschke

This course introduces students to the field of computer forensics and it will focus on the various contemporary policy issues and applied technologies. Topics to be covered include: legal and regulatory issues, investigation techniques, data analysis approaches, and incident response procedures for Windows and UNIX systems. Homework in this course will relate to laboratory assignments and research exercises. Students should also expect that a group project will be integrated into this course.

W 6:40-9:10p
limit 40
in person

650.658

INTRODUCTION TO CRYPTOGRAPHY Xiangyang Li

Cryptography has a rich history as one of the foundations of information security. This course serves as the introduction to the working primitives, development and various techniques in this field. It emphasizes reasoning about the constraint and construction of cryptographic protocols that use shared secret key or public key. Students will also be exposed to some current open problems. Permission of instructor only.

TuTh 1:30-2:45p
limit 40
in person

650.660

SOFTWARE VULNERABILITY ANALYSIS Reuben Johnston

Competent execution of security assessments on modern software systems requires extensive knowledge in numerous technical domains and comprehensive understanding of security risks. This course provides necessary background knowledge and examines relevant theories for software vulnerabilities and exploits in detail. Key topics include historical vulnerabilities, their corresponding exploits, and associated risk mitigations. Fundamental tools and techniques for performing security assessments (e.g., software reverse engineering, static analysis, and dynamic analysis) are covered extensively. The format of this course includes lectures and assignments where students learn how to develop exploits to well-known historical vulnerabilities in a controlled environment. Students will complete and demonstrate a project as part of the course.

TuTh 3-4:15p
limit 40
in person

650.673

MOBILE AND WIRELESS SECURITY Ashutosh Dutta

The past few decades have seen a rapid evolution of wireless LAN and cellular technologies. In addition to wireless access technologies, various types of network layer and application layer mobility protocols have been developed to provide seamless connectivity to mobile users. Maintaining end-to-end security for these mobile users needs to take into account authentication, authorization, integrity and confidentiality as mobile devices change their point-of-attachment. This course will provide an overview of various wireless access technologies, mobility protocol taxonomy and will describe end-to-end security including mobile end point, radio access network, network core, and application services. In addition, this will include hands-on lab experiments to examine security over wireless and mobile networks and a research group project. Overall objective of this course is to impart both theoretical and practical knowledge to the students, and at the same time make them ready for any future research to solve complex problems. Recommended Course Background: Knowledge of TCP/IP, Linux, Fundamentals of Networking.

Fr 4:15-6:45
limit 25
in person

650.836

INFORMATION SECURITY PROJECTS Dahbura, Li

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 course for any full-time MSSI student. Open to MSSI students. Permission required for non-MSSI students.

MWF 10
limit 49
in person

650.840

INFORMATION SECURITY INDEPENDENT STUDY Xiangyang Li

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