Cat. I
This course introduces students to the fundamental principles of
programming in imperative and scripting languages. Topics include control
structures, iterators, functional decomposition, basic data structures (such
as records). Students will be expected to implement, test and debug
programs. Through the use of compelling applications and lab exercises,
students will learn how to interface with external data systems and control
devices.
Recommended background: none.
All Computer Science students and other students wishing to prepare for
3000level courses in Computer Science should take CS 1101/1102
instead of CS 1004. This course provides sufficient background for CS
2301 Systems Programming for NonMajors.
Undergraduate Courses
CS 1101. INTRODUCTION TO PROGRAM DESIGN

This course introduces principles of computation and programming with an emphasis on program design. Topics include the design, implementation and testing of programs that use a variety of data structures (such as structures, lists, and trees), functions, conditionals, recursion and higher‐order functions. Students will be expected to design simple data models, and implement and debug programs in a functional programming language. Recommended background: none. Either CS 1101 or CS 1102 provides sufficient background for further courses in the CS department. Undergraduate credit may not be earned for both this course and CS 1102.
CS 1102. ACCELERATED INTRODUCTION TO PROGRAM DESIGN

In the first half of the term, this course covers the same functional programming material as CS 1101 at roughly twice the pace. The second half of the term is a preview of selected advanced Computer Science topics, such as the design and implementation of applicationspecific languages, macros, programming with the HTTP protocol and continuationpassing style. Students will be expected to complete an openended individual programming project. Recommended background: Substantial prior programming experience (including functions, recursion, and lists, as would be covered in highschool Advanced Placement Computer Science A courses, but not necessarily AP CS Principles courses). Either CS 1101 or CS 1102 provides sufficient background for further courses in the CS department. Undergraduate credit may not be earned for both this course and CS 1101.
CS 2011. INTRODUCTION TO MACHINE ORGANIZATION AND ASSEMBLY LANGUAGE

Cat. I This course introduces students to the structure and behavior of modern digital computers and the way they execute programs. Machine organization topics include the Von Neumann model of execution, functional organization of computer hardware, the memory hierarchy, caching performance, and pipelining. Assembly language topics include representations of numbers in computers, basic instruction sets, addressing modes, stacks and procedures, lowlevel I/O, and the functions of compilers, assemblers, linkers, and loaders. The course also presents how code and data structures of higherlevel languages are mapped into the assembly language and machine representations of a modern processor. Programming projects will be carried out in the C language and the assembly language of a modern processor. Recommended background: CS 2301 or CS 2303, or a significant knowledge of C/C++.
CS 2022. DISCRETE MATHEMATICS

Cat. I This course serves as an introduction to some of the more important concepts, techniques, and structures of discrete mathematics, providing a bridge between computer science and mathematics. Topics include sets, functions and relations, propositional and predicate calculus, mathematical induction, properties of integers, counting techniques and graph theory. Students will be expected to develop simple proofs for problems drawn primarily from computer science and applied mathematics. Recommended background: none.
CS 2102. OBJECTORIENTED DESIGN CONCEPTS

Cat. I This course introduces students to an objectoriented model of programming. Building from the design methodology covered in CS 1101/CS 1102, this course shows how programs can be decomposed into classes and objects. By emphasizing design, this course shows how to implement small defectfree programs and evaluate design decisions to select an optimal design under specific assumptions. Topics include inheritance, exceptions, interface, design by contract, basic design patterns, and reuse. Students will be expected to design, implement, and debug objectoriented programs composed of multiple classes and over a variety of data structures. Recommended background: CS 1101 or CS 1102.
CS 2103. ACCELERATED OBJECTORIENTED DESIGN CONCEPTS

This course covers the data structures and general programdesign material from CS2102, but assumes that students have significant prior experience in objectoriented programming. The course covers objectoriented design principles and data structures more deeply and at a faster pace than in CS 2102. Students will be expected to design, implement, test, debug, and critique programs both for correctness and adherence to good objectoriented design principles. The course is designed to strengthen both the design skills and algorithmic thinking of students who already have a foundation in objectoriented programming. Recommended background: CS 1101 or CS 1102 and significant prior experience writing objectoriented programs from scratch. Advanced Placement Computer Science A courses should provide sufficient background; students from AP CS Principles courses or gentler introductions to Java Programming are advised to take CS2102 instead. Students may receive credit for only one of the following three courses: CS 2102, CS 210X, CS 2103.
CS 2119. APPLICATION BUILDING WITH OBJECTORIENTED CONCEPTS

Cat. I This course introduces students to an objectoriented model of programming, with an emphasis on the programming approaches useful in creating software applications. Students will be expected to design, implement, and debug objectoriented programs. Topics include inheritance, user interfaces, and database access. This course is for nonCS majors with prior programming experience and an interest in building software applications. Recommended background: Some programming experience such as found in CS 1004, CS 1101, or CS 1102.
CS 2223. ALGORITHMS

Cat. I Building on a fundamental knowledge of data structures, data abstraction techniques, and mathematical tools, a number of examples of algorithm design and analysis, worst case and average case, will be developed. Topics include greedy algorithms, divideandconquer, dynamic programming, heuristics, and probabilistic algorithms. Problems will be drawn from areas such as sorting, graph theory, and string processing. The influence of the computational model on algorithm design will be discussed. Students will be expected to perform analysis on a variety of algorithms. Recommended background: CS 2102 and CS 2022.
CS 2301. SYSTEMS PROGRAMMING FOR NONMAJORS

Cat. I This course introduces the C programming language and system programming concepts to nonCS majors who need to program computers in their own fields. The course assumes that students have had previous programming experience. It quickly introduces the major concepts of the C language and covers manual memory management, pointers and basic data structures, the machine stack, and input/output mechanisms. Students will be expected to design, implement, and debug programs in C. Recommended background: CS 1101 or CS 1102 or previous experience programming a computer. All Computer Science students and other students wishing to prepare for upperlevel courses in Computer Science should take CS 2303 instead of CS 2301. Students who have credit for CS 2303 may not receive subsequent credit for CS 2301.
CS 2303. SYSTEMS PROGRAMMING CONCEPTS

Cat. I This course introduces students to a model of programming where the programming language exposes details of how the hardware stores and executes software. Building from the design concepts covered in CS 2102, this course covers manual memory management, pointers, the machine stack, and input/ output mechanisms. The course will involve largescale programming exercises and will be designed to help students confront issues of safe programming with systemlevel constructs. The course will cover several tools that assist programmers in these tasks. Students will be expected to design, implement, and debug programs in C++ and C. The course presents the material from CS 2301 at a fast pace and also includes C++ and other advanced topics. Recommended background: CS 2102 and/or substantial objectoriented programming experience.
CS 3013. OPERATING SYSTEMS

Cat. I This course provides the student with an understanding of the basic components of a generalpurpose operating system. Topics include processes, process management, synchronization, input/output devices and their programming, interrupts, memory management, resource allocation, and an introduction to file systems. Students will be expected to design and implement a large piece of system software in the C programming language. Undergraduate credit may not be earned both for this course and for CS 502. Recommended background: CS 2303 or CS 2301, and CS 2011.
CS 3041. HUMANCOMPUTER INTERACTION

Cat. I This course develops in the student an understanding of the nature and importance of problems concerning the efficiency and effectiveness of human interaction with computerbased systems. Topics include the design and evaluation of interactive computer systems, basic psychological considerations of interaction, interactive language design, interactive hardware design, and special input/output techniques. Students will be expected to complete several projects. A project might be a software evaluation, interface development, or an experiment. Recommended background: CS 2102 or CS 2118.
CS 3043. SOCIAL IMPLICATIONS OF INFORMATION PROCESSING

Cat. I This course makes the student aware of the social, moral, ethical, and philosophical impact of computers and computerbased systems on society, both now and in the future. Topics include major computerbased applications and their impact, humanmachine relationships, and the major problems of controlling the use of computers. Students will be expected to contribute to classroom discussions and to complete a number of significant writing assignments. This course is recommended for juniors and seniors. Undergraduate credit may not be earned both for this course and for CS 505. Recommended background: a general knowledge of computers and computer systems.
CS 3133. FOUNDATIONS OF COMPUTER SCIENCE

Cat. I This course introduces the theoretical foundations of computer science. These form the basis for a more complete understanding of the proficiency in computer science. Topics include computational models, formal languages, and an introduction to compatibility and complexity theory, including NPcompleteness. Students will be expected to complete a variety of exercises and proofs. Undergraduate credit may not be earned both for this course and for CS 5003. Recommended Background: Discrete mathematics (CS 2022 or equivalent) and algorithms (CS 2223 or equivalent). Students who have credit for CS 4121 cannot receive credit for CS 3133.
CS 3431. DATABASE SYSTEMS I

Cat. I This course introduces the student to the design, use, and application of database management systems. Topics include the relational data model, relational query languages, design theory, and conceptual data design and modeling for relational database design. Techniques that provide for data independence, and minimal redundancy will be discussed. Students will be expected to design and implement database system applications. Undergraduate credit may not be earned both for this course and for CS 4431 or CS 542. Recommended background: CS 2022 and either CS 2102 or CS 2118.
CS 3516. COMPUTER NETWORKS

Cat. I This course provides a broad view of computer networks. The course exposes students to all seven layers of OSI Reference Model while providing an introduction into newer topics such as wireless networking and Internet traffic concerns. The objective is to focus on an understanding of fundamental concepts of modern computer network architecture from a design and performance perspective. Topics covered include: physical layer considerations, network protocols, wide area networks, local area networks, wireless networks, switches and routing, congestion, Internet traffic and network security. Students will be expected to do extensive systems/network programming and will be expected to make use of simulation and measurement tools to gain an appreciation of current network design and performance issues. This course is also highly recommended for RBE and IMGD majors. Recommended background: CS 2301 or CS 2303, or a significant knowledge of C/C++.
CS 3733. SOFTWARE ENGINEERING

Cat. I This course introduces the fundamental principles of software engineering. Modern software development techniques and life cycles are emphasized. Topics include requirements analysis and specification, analysis and design, architecture, implementation, testing and quality, configuration management, and project management. Students will be expected to complete a project that employs techniques from the topics studied. This course should be taken before any course requiring a large programming project. Undergraduate credit may not be earned both for this course and for CS 509. Recommended background: CS 2102.
CS 4032. NUMERICAL METHODS FOR LINEAR AND NONLINEAR SYSTEMS

Cat. I This course provides an introduction to modern computational methods for linear and nonlinear equations and systems and their applications. Topics covered include: solution of nonlinear scalar equations, direct and iterative algorithms for the solution of systems of linear equations, solution of nonlinear systems, the eigenvalue problem for matrices. Error analysis will be emphasized throughout. Recommended background: MA 2071. An ability to write computer programs in a scientific language is assumed.
CS 4033. NUMERICAL METHODS FOR CALCULUS AND DIFFERENTIAL EQUATIONS

Cat. I This course provides an introduction to modern computational methods for differential and integral calculus and differental equations. Topics covered include: interpolation and polynomial approximation, approximation theory, numerical differentiation and integration, numerical solutions of ordinary differential equations. Error analysis will be emphasized throughout. Recommended background: MA 2051. An ability to write computer programs in a scientific language is assumed. Undergraduate credit may not be earned for both this course and for MA 3255/CS 4031.
CS 4100. ARTIFICIAL INTELLIGENCE FOR INTERACTIVE MEDIA AND GAMES

Cat. II Algorithms and programming techniques from artificial intelligence (AI) are key contributors to the experience of modern computer games and interactive media, either by directly controlling a nonplayer character (NPC) or through more subtle manipulation of the environment. This course will focus on the practical AI programming techniques currently used in computer games for NPC navigation and decisionmaking, along with the design issues that arise when AI is applied in computer games, such as believability and realtime performance. The course will also briefly discuss future directions in applying AI to games and media. Students will be expected to complete significant software development projects using the studied techniques. Recommended background: objectoriented design concepts (CS 2102), algorithms (CS 2223), and knowledge of technical game development (IMGD 3000). This course will be offered in 201617, and in alternating years thereafter.
CS 4120. ANALYSIS OF ALGORITHMS

Cat. II This course develops the skill of analyzing the behavior of algorithms. Topics include the analysis, with respect to average and worst case behavior and correctness, of algorithms for internal sorting, pattern matching on strings, graph algorithms, and methods such as recursion elimination, dynamic programming, and program profiling. Students will be expected to write and analyze programs. Undergraduate credit may not be earned both for this course and for CS 5084. Recommended background: algorithms (CS 2223 or equivalent), and some knowledge of probability. This course will be offered in 202021, and in alternating years thereafter.
CS 4123. THEORY OF COMPUTATION

Cat. II Building on the theoretical foundations from CS 3133, this course addresses the fundamental question of what it means to be “computable,” including different characterization of computable sets and functions. Topics include the halting program, the ChurchTuring thesis, primitive recursive functions, recursive sets, recursively enumerable sets, NPcompleteness, and reducibilities. Students will be expected to complete a variety of exercises and proofs. Recommended Background: CS 3133. This course will be offered in 201516, and in alternating years thereafter.
CS 4233. OBJECTORIENTED ANALYSIS AND DESIGN

Cat. II This Software Engineering course will focus on the process of ObjectOriented Analysis and Design. Students will be expected to complete a large number of exercises in Domain Modeling, Use Case Analysis, and ObjectOriented Design. In addition, the course will investigate Design Patterns, which are elements of reusable objectoriented software designs. This course will survey a set of design patterns and consider how these patterns are described and used to solve design problems. Recommended Background: CS 2303 and CS 3733. This course will be offered in 201617, and in alternating years thereafter.
CS 4241. WEBWARE: COMPUTATIONAL TECHNOLOGY FOR NETWORK INFORMATION SYSTEMS

Cat. I This course explores the computational aspects of network information systems as embodied by the World Wide Web (WWW). Topics include: languages for document design, programming languages for executable content, scripting languages, design of WWW based human/computer interfaces, client/server network architecture models, high level network protocols (e.g., http), WWW network resource discovery and network security issues. Students in this course will be expected to complete a substantial software project (e.g., Java based user interface, HTML/CGI based information system, WWW search mechanisms). Recommended background: CS 2102 and CS 3013.
CS 4341. INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Cat. I This course studies the problem of making computers act in ways which we call "intelligent". Topics include major theories, tools and applications of artificial intelligence, aspects of knowledge representation, searching and planning, and natural language understanding. Students will be expected to complete projects which express problems that require search in state spaces, and to propose appropriate methods for solving the problems. Undergraduate credit may not be earned both for this course and for CS 534. Recommended background: CS 2102, CS 2223, and CS 3133.
CS 4342. MACHINE LEARNING

Cat.I In this course, students will explore both theoretical and practical aspects of machine learning, including algorithms for regression, classification, dimensionality reduction, clustering, and density estimation. Specific topics may include neural networks and deep learning, Bayesian networks and probabilistic graphical models, principal component analysis, kmeans clustering, decision trees and random forests, support vector machines, and kernel methods. Recommended background: Multivariate Calculus (MA 1024 or MA 1034), Linear Algebra (such as MA 2071), Probability (MA 2621 or MA 2631), and Algorithms (CS 2223). Students may not earn credit for both CS 453X and CS 4342. Undergraduate credit may not be earned both for this course and for CS 539.
CS 4401. SOFTWARE SECURITY ENGINEERING

Cat. I This course provides an introduction to the pitfalls and practices of building secure software applications. Topics will include threat modeling, secure software development, defensive programming, web security and the interaction between security and usability. The course focuses on the application level with minor attention to operatingsystem level security; networklevel security is not covered. Assignments involve designing and implementing secure software, evaluating designs and systems for securityrelated flaws, and presentations on security issues or tools. All students will be required to sign a pledge of responsible conduct at the start of the course. Recommended Background: CS3013 and CS3733. The course assumes nontrivial experience with C and Unix, familiarity with operating systems, filesystems, and databases, and experience with technologies for building web applications (from CS4241 or personal experience).
CS 4404. TOOLS AND TECHNIQUES IN COMPUTER NETWORK SECURITY

This course introduces students to modern network security concepts, tools, and techniques. The course covers security threats, attacks and mitigations at the operatingsystem and network levels (as opposed to the software level). Topics include: authentication, authorization, confidentiality, integrity, anonymity, privacy, intrusion detection and response, and cryptographic applications. Students will become familiar with modern security protocols and tools. Assignments will involve using securitytesting software to uncover vulnerabilities, network packet analyzers, and existing security applications to create secure network implementations. The course requires enough programming and systems background to understand attacks and use systems tools, but does not involve significant programming projects. Assignments and projects will use a Linux base for implementation. Recommended Background: Knowledge of operating systems (CS3013 or equivalent) and computer networks (CS3516 or equivalent). Familiarity with Linux or Unix is essential.
CS 4432. DATABASE SYSTEMS II

Cat. II This course concentrates on the study of the internals of database management systems. Topics include: principles and theories of physical storage management, advanced query languages, query processing and optimization, index structures for relational databases, transaction processing, concurrency control, distributed databases, and database recovery, security, client server and transaction processing systems. Students may be expected to design and implement software components that make up modern database systems. Undergraduate credit may not be earned both for this course and CS 542. Recommended background: CS 3431 and CS 3733. This course will be offered in 201516, and in alternating years thereafter.
CS 4445. DATA MINING AND KNOWLEDGE DISCOVERY IN DATABASES

Cat. II This course provides an introduction to Knowledge Discovery in Databases (KDD) and Data Mining. KDD deals with data integration techniques and with the discovery, interpretation and visualization of patterns in large collections of data. Topics covered in this course include data warehousing and mediation techniques; data mining methods such as rulebased learning, decision trees, association rules and sequence mining; and data visualization. The work discussed originates in the fields of artificial intelligence, machine learning, statistical data analysis, data visualization, databases, and information retrieval. Several scientific and industrial applications of KDD will be studied. Recommended background: MA 2611, CS 2223, and CS 3431, or CS 3733. This course will be offered in 201617, and in alternating years thereafter.
CS 4513. DISTRIBUTED COMPUTING SYSTEMS

Cat. II This course extends the study of the design and implementation of operating systems begun in CS 3013 to distributed and advanced computer systems. Topics include principles and theories of resource allocation, file systems, protection schemes, and performance evaluation as they relate to distributed and advanced computer systems. Students may be expected to design and implement programs that emphasize the concepts of file systems and distributed computing systems using current tools and languages. Undergraduate credit may not be earned both for this course and for CS 502. Recommended background: CS 3013, CS 3516, and system programming experience. This course will be offered in 201516, and in alternating years thereafter.
CS 4515. COMPUTER ARCHITECTURE

Cat. II This course explores the architectural design of modern computer systems in terms of instruction sets and the organization of processors, controllers, memories, devices, and communication links. Topics include an overview of computer architectures and system components, theoretical foundations, instructionlevel and threadlevel pipelining, multifunction pipelines, multicore systems, caching and memory hierarchies, and multicore and parallel computer organization. Students may be expected to design and implement programs that simulate significant components of modern computer architectures. Recommended background: CS 2011 or ECE 2049, and CS 3013. This course will be offered in 201617, and in alternating years thereafter.
CS 4516. ADVANCED COMPUTER NETWORKS

Cat. II This course provides an indepth look into computer networks. While repeating some of the areas from CS 3516, the goal is to go deeper into computer networks topics. This indepth treatment in topics such as routing, congestion control, wireless layer protocols and physical signaling considerations will require the use of basic queuing theory and probability to provide a more formal treatment of computer networks performance. Other topics covered include: LAN and WLAN technologies, mobile wireless networks, sensor networks, optical networks, network security, intrusion detection and network management. Students will be expected to do more sophisticated network programming than seen in CS 3516 and will conduct laboratory activities involving measuring the performance of modern networking applications running on both wired networks and infrastructure wireless networks. Undergraduate credit may not be earned both for this course and for CS 513. Recommended background: CS 3013, CS 3516, and knowledge of probability. The course assumes a familiarity with operating systems including Unix or Linux, and significant experience with C/C++. This course will be offered in 201617, and in alternating years thereafter.
CS 4518. MOBILE & UBIQUITOUS COMPUTING

The goal of this course is to acquaint students with fundamental concepts and stateoftheart computer science literature in mobile and ubiquitous computing. Topics to be covered include mobile systems issues, human activity and emotion sensing, location sensing, mobile humancomputer interaction, mobile social networking, mobile health, power saving techniques, energy and mobile performance measurement studies and mobile security. The course will introduce the programming of mobile devices such as smartphones running the Android operating system. Recommended background: Proficiency in programming in Java, including classes, inheritance, exceptions, interfaces, polymorphism (CS 2012 or equivalent). Students may not earn credit for both CS 403X and CS 4518
CS 4533. TECHNIQUES OF PROGRAMMING LANGUAGE TRANSLATION

Cat. II This course studies the compiling process for highlevel languages. Topics include lexical analysis, syntax analysis, semantic analysis, symbol tables, intermediate languages, optimization, code generation and runtime systems. Students will be expected to use compiler tools to implement the front end, and to write a program to implement the back end, of a compiler for a recursive programming language. Undergraduate credit may not be earned for both this course and for CS 544. Recommended Background: CS 2102 and CS 3133. This course will be offered in 201617, and in alternating years thereafter.
CS 4536. PROGRAMMING LANGUAGES

Cat. II This course covers the design and implementation of programming languages. Topics include data structures for representing programming languages, implementing control structures (such as functions, recursion, and exceptions), garbage collection, and type systems. Students will be expected to implement several small languages using a functional programming language. Recommended background: CS 2303, CS 3133, and experience programming in a functional language (as provided by CS 1101 or CS 1102). Undergraduate credit may not be earned for both this course and CS 536. This course will be offered in 201617, and in alternating years thereafter.
CS 453X. MACHINE LEARNING

In this course, students will explore both theoretical and practical aspects of machine learning, including algorithms for regression, classification, dimensionality reduction, clustering, and density estimation. Specific topics may include: neural networks and deep learning, Bayesian networks and probabilistic graphical models, principal component analysis, kmeans clustering, decision trees and random forests, support vector machines and kernel methods. Recommended background: Knowledge of Multivariate Calculus (MA 1024 or MA 1034), Linear Algebra (such as MA 2071), Probability (MA 2621 or MA 2631), and Algorithms (CS 2223).
CS 4731. COMPUTER GRAPHICS

Cat. I This course studies the use of the computer to model and graphically render two and threedimensional structures. Topics include graphics devices and languages, 2 and 3D object representations, and various aspects of rendering realistic images. Students will be expected to implement programs which span all stages of the 3D graphics pipeline, including clipping, projection, arbitrary viewing, hidden surface removal and shading. Undergraduate credit may not be earned both for this course and for CS 543. Recommended background: CS 2223, CS 2303 and MA 2071.
CS 4801. INTRODUCTION TO CRYPTOGRAPHY AND COMMUNICATION SECURITY

This course provides an introduction to modern cryptography and communication security. It focuses on how cryptographic algorithms and protocols work and how to use them. The course covers the concepts of block ciphers and message authentication codes, public key encryption, digital signatures, and key establishment, as well as common examples and uses of such schemes, including the AES, RSAOAEP, and the Digital Signature Algorithm. Basic cryptanalytic techniques and examples of practical security solutions are explored to understand how to design and evaluate modern security solutions. The course is suited for students interested in cryptography or other security related fields such as trusted computing, network and OS security, or general IT security. Recommended background: ECE 2049 Embedded Computing in Engineering Design or CS 2301 Systems Programming for NonMajors or equivalent Suggested background: CS 2022/MA2201 Discrete Mathematics
CS 4802. BIOVISUALIZATION

Cat. II This course will use interactive visualization to model and analyze biological information, structures, and processes. Topics will include the fundamental principles, concepts, and techniques of visualization (both scientific and information visualization) and how visualization can be used to study bioinformatics data at the genomic, cellular, molecular, organism, and population levels. Students will be expected to write small to moderate programs to experiment with different visual mappings and data types. Recommended background: CS 2102, CS 2223, and one or more biology courses. This course will be offered in 201617, and in alternating years thereafter.
CS 4803. BIOLOGICAL AND BIOMEDICAL DATABASE MINING

Cat. II This course will investigate computational techniques for discovering patterns in and across complex biological and biomedical sources including genomic and proteomic databases, clinical databases, digital libraries of scientific articles, and ontologies. Techniques covered will be drawn from several areas including sequence mining, statistical natural language processing and text mining, and data mining. Recommended Background: CS 2102, CS 2223, MA 2610 or MA 2611, and one or more biology courses. This course will be offered in 201516, and in alternating years thereafter.
CS 480X. DATA VISUALIZATION

This course exposes students to the field of data visualization, i.e., the graphical communication of data and information for the purposes of presentation, confirmation, and exploration. The course introduces the stages of the visualization pipeline. This includes data modeling, mapping data attributes to graphical attributes, visual display techniques, tools, paradigms, and perceptual issues. Students learn to evaluate the effectiveness of visualizations for specific data, task, and user types. Students implement visualization algorithms and undertake projects involving the use of commercial and publicdomain visualization tools. Recommended background: Knowledge of Linear Algebra (such as MA 2071), Probability theory (MA 2621), and Software Engineering (CS 3733).
Graduate Courses
CS 5003. FOUNDATIONS OF COMPUTER SCIENCE: AN INTRODUCTION

This is the study of mathematical foundations of computing, at a slower pace than that of CS 503 and with correspondingly fewer background assumptions. Topics include finite automata and regular languages, pushdown automata and contextfree languages, Turing machines and decidability, and an introduction to computational complexity. (Prerequisite: an undergraduate course in discrete mathematics.)
CS 5007. INTRODUCTION TO PROGRAMMING CONCEPTS, DATA STRUCTURES AND ALGORITHMS

This is an introductory graduate course teaching core computer science topics typically found in an undergraduate Computer Science curriculum, but at a graduatelevel pace. It is primarily intended for students with little formal preparation in Computer Science to gain experience with fundamental Computer Science topics. After a review of programming concepts the focus of the course will be on data structures from the point of view of the operations performed upon the data and to apply analysis and design techniques to nonnumeric algorithms that act on data structures. The data structures covered include lists, stacks, queues, trees and graphs. Projects will focus on the writing of programs to appropriately integrate data structures and algorithms for a variety of applications. This course may not be used to satisfy degree requirements for a B.S., M.S., or Ph.D. degree in Computer Science or a minor in Computer Science. It may satisfy the requirements for other degree programs at the discretion of the program review committee for the particular degree. (Prerequisites: Experience with at least one highlevel programming language such as obtained in an undergraduate programming course.)
CS 502. OPERATING SYSTEMS

The design and theory of multiprogrammed operating systems, concurrent processes, process communication, input/output supervisors, memory management, resource allocation and scheduling are studied. (Prerequisites: knowledge of computer organization and elementary data structures, and a strong programming background.)
CS 503. FOUNDATIONS OF COMPUTER SCIENCE

This is the study of mathematical foundations of computing. Topics include finite automata and regular languages, pushdown automata and contextfree languages, Turing machines and decidability, and an introduction to computational complexity. (Prerequisites: Knowledge of discrete mathematics and algorithms at the undergraduate level, and some facility with reading and writing mathematical proofs.)
CS 504. ANALYSIS OF COMPUTATIONS AND SYSTEMS

The following tools for the analysis of computer programs and systems are studied: probability, combinatorics, the solution of recurrence relations and the establishment of asymptotic bounds. A number of algorithms and advanced data structures are discussed, as well as paradigms for algorithm design. (Prerequisites: CS 5084 or equivalent.)
CS 5084. INTRODUCTION TO ALGORITHMS: DESIGN AND ANALYSIS

This course is an introduction to the design, analysis and proofs of correctness of algorithms. Examples are drawn from algorithms for many areas. Analysis techniques include asymptotic worst case and average case, as well as amortized analysis. Average case analysis includes the development of a probability model. Techniques for proving lower bounds on complexity are discussed, along with NPcompleteness. Prerequisites: an undergraduate knowledge of discrete mathematics and data structures. Note: students with a strong background in design and analysis of computer systems, at the level equal to a BS in computer science, should not take CS 5084 and should consider taking CS 504 or CS 584.
CS 509. DESIGN OF SOFTWARE SYSTEMS

This course introduces students to a methodology and specific design techniques for teambased development of a software system. Against the backdrop of the software engineering lifecycle, this course focuses on the objectoriented paradigm and its supporting processes and tools. Students will be exposed to industrialaccepted standards and tools, such as requirements elicitation, specification, modeling notations, design patterns, software architecture, integrated development environments and testing frameworks. Students will be expected to work together in teams in the complete specification, implementation and testing of a software application. Prerequisites: knowledge of a recursive highlevel language and data structures. An undergraduate course in software engineering is desirable.
CS 513. COMPUTER NETWORKS

This course provides an introduction to the theory and practice of the design of computer and communications networks, including the ISO sevenlayer reference model. Analysis of network topologies and protocols, including performance analysis, is treated. Current network types including local area and wide area networks are introduced, as are evolving network technologies. The theory, design and performance of local area networks are emphasized. The course includes an introduction to queueing analysis and network programming. (Prerequisites: knowledge of the C programming language is assumed. CS 504 or ECE 502 or equivalent background in CS 5084 or CS 584.)
CS 514. ADVANCED SYSTEMS ARCHITECTURE

This Course covers techniques such as caching, hierarchical memory, pipelining and parallelism, that are used to enhance the performance of computer systems. It compares and contrast different approaches to achieving high performance in machine ranging from advanced microprocessors to vector supercomputers (CRAY, CYBER). It also illustrates how these techniques are applied in massively parallel SIMD machines (DAP, Connection Machine). In each case the focus is on the combined hardware/ software performance achieved and the interaction between application demands and hardware/software capabilities. (Prerequisites: This course assumes the material covered in ECE 505. The student should also have a background in computer programming and operating systems (CS 502). Familiarity with basic probability and statistics such as ECE 502 or MA 541 is recommended.
CS 521. LOGIC IN COMPUTER SCIENCE

This course is an introduction to mathematical logic from a computer science perspective. Topics covered include the exploration of model theory, proof theory, and decidability for propositional and firstorder classical logics, as well as various nonclassical logics that provide useful tools for computer science (such as temporal and intuitionistic logics). The course stresses the application of logic to various areas of computer science such as computability, theorem proving, programming languages, specification, and verification. The specific applications included will vary by instructor. (Prerequisites: CS 503, or equivalent background in basic models of computation.)
CS 522. NUMERICAL METHODS

This course provides an introduction to a broad range of modern numerical techniques that are widely used in computational mathematics, science, and engineering. It is suitable for both mathematics majors and students from other departments. It covers introductorylevel material for subjects treated in greater depth in MA 512 and MA 514 and also topics not addressed in either of those courses. Subject areas include numerical methods for systems of linear numerical methods for systems of linear and nonlinear equations, interpolation and approximation, differentiation and integration, and differential equations. Specific topics include basic direct and iterative methods for linear systems; classical rootfinding methods; Newton’s method and related methods for nonlinear systems; fixedpoint iteration; polynomial, piecewise polynomial, and spline interpolation methods: leastsquares approximation; orthogonal functions and approximation; basic techniques for numerical differentiation; numerical integration, including adaptive quadrature; and methods for initialvalue problems for ordinary differential equations. Additional topics may be included at the instructor’s discretion as time permits. Both theory and practice are examined. Error estimates, rates of convergence, and the consequences of finite precision arithmetic are also discussed. Topics from linear algebra and elementary functional analysis will be introduced as needed. These may include norms and inner products, orthogonality and orthogonalization, operators and projections, and the concept of a function space. (Prerequisite: knowledge of undergraduate linear algebra and differential equations is assumed, as is familiarity with MATLAB or a higherlevel programming language.)
CS 528. MOBILE AND UBIQUITOUS COMPUTING

This course acquaints participants with the fundamental concepts and stateoftheart computer science research in mobile and ubiquitous computing. Topics covered include mobile systems issues, human activity and emotion sensing, location sensing, mobile HCI, mobile social networking, mobile health, power saving techniques, energy and mobile performance measurement studies and mobile security. The course consists of weekly presentations on current advanced literature, discussions and a term project. The term project involves implementing research ideas on a mobile device such as a smartphone. Prerequisite: CS 502 or an equivalent graduate level course in Operating Systems, and CS 513 or an equivalent graduate level course in Computer Networks, and proficiency in a high level programming language.
CS 529. MULTIMEDIA NETWORKING

This course covers basic and advanced topics related to using computers to support audio and video over a network. Topics related to multimedia will be selected from areas such as compression, network protocols, routing, operating systems and human computer interaction. Students will be expected to read assigned research papers and complete several programming intensive projects that illustrate different aspects of multimedia computing. (Prerequisites: CS 502 and CS 513 or the equivalent and strong programming skills.)
CS 534. ARTIFICIAL INTELLIGENCE

This course gives a broad survey of artificial intelligence. The course will cover methods from search, probabilistic reasoning, and learning, among other topics. Selected topics involving the applications of these tools are investigated. Such topics might include natural language understanding, scene understanding, game playing, and planning. (Prerequisites: familiarity with data structures and a highlevel programming language.)
CS 535. ADVANCED TOPICS IN OPERATING SYSTEMS

This course discusses advanced topics in the theory, design and implementation of operating systems. Topics will be selected from such areas as performance of operating systems, distributed operating systems, operating systems for multiprocessor systems and operating systems research. (Prerequisites: CS 502 and either CS 5084, CS 504, CS 584, or equivalent background in probability.) See the SUPPLEMENT section of the online catalog at www.wpi.edu/+gradcat for descriptions of courses to be offered in this academic year.
CS 539. MACHINE LEARNING

The focus of this course is machine learning for knowledgebased systems. It will include reviews of work on similaritybased learning (induction), explanationbased learning, analogical and casebased reasoning and learning, and knowledge compilation. It will also consider other approaches to automated knowledge acquisition as well as connectionist learning. (Prerequisite: CS 534 or equivalent, or permission of the instructor.)
CS 541. DEEP LEARNING

This course will offer a mathematical and practical perspective on artificial neural networks for machine learning. Students will learn about the most prominent network architectures including multilayer feedforward neural networks, convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), and generativeadversarial networks (GANs). This course will also teach students optimization and regularization techniques used to train them  such as backpropagation, stochastic gradient descent, dropout, pooling, and batch normalization. Connections to related machine learning techniques and algorithms, such as probabilistic graphical models, will be explored. In addition to understanding the mathematics behind deep learning, students will also engage in handson course projects. Students will have the opportunity to train neural networks for a wide range of applications, such as object detection, facial expression recognition, handwriting analysis, and natural language processing. Prerequisite: Machine Learning (CS 539), and knowledge of Linear Algebra (such as MA 2071) and Algorithms (such as CS 2223).
CS 542. DATABASE MANAGEMENT SYSTEMS

An introduction to the theory and design of database management systems. Topics covered include internals of database management systems, fundamental concepts in database theory, and database application design and development. In particular, logical design and conceptual modeling, physical d atabase design strategies, relational data model and query languages, query optimization, transaction management and distributed databases. Typically there are handson assignments and/or a course project. Selected topics from the current database research literature may be touched upon as well. (Prerequisite: CS 5084, CS 504, or CS 584.)
CS 543. COMPUTER GRAPHICS

This course examines typical graphics systems, both hardware and software; design of lowlevel software support for raster displays; 3D surface and solids modeling; hidden line and hidden surface algorithms; and realistic image rendering including shading, shadowing, reflection, refraction and surface texturing. (Prerequisites: familiarity with data structures, a recursive highlevel language and linear algebra. CS 509 would be helpful.)
CS 544. COMPILER CONSTRUCTION

A general approach to the design of language processors is presented without regard for either the source language or target machine. All phases of compilation and interpretation are investigated in order to give the student an appreciation for the overall construction of a compiler. Typical projects may include implementation of a small compiler for a recursive or specialpurpose language. (Prerequisites: knowledge of several higherlevel languages and at least one assembly language. The material in CS 503 is helpful.)
CS 545. DIGITAL IMAGE PROCESSING

This course presents fundamental concepts of digital image processing and an introduction to machine vision. Image processing topics will include visual perception, image formation, imaging geometries, image transform theory and applications, enhancement, restoration, encoding and compression. Machine vision topics will include feature extraction and representation, stereo vision, modelbased recognition, motion and image flow, and pattern recognition. Students will be required to complete programming assignments in a highlevel language. (Prerequisites: working knowledge of undergraduate level signal analysis and linear algebra; familiarity with probability theory is helpful but not necessary.)
CS 546. HUMANCOMPUTER INTERACTION

This course prepares graduate students for research in humancomputer interaction. Topics include the design and evaluation of interactive computer systems, basic psychological considerations of interaction, interactive language design, interactive hardware design and special input/output techniques. Students are expected to present and review recent research results from the literature, and to complete several projects. (Prerequisites: students are expected to have mature programming skills. Knowledge of software engineering would be an advantage.)
CS 548. KNOWLEDGE DISCOVERY AND DATA MINING

This course presents current research in Knowledge Discovery in Databases (KDD) dealing with data integration, mining, and interpretation of patterns in large collections of data. Topics include data warehousing and data preprocessing techniques; data mining techniques for classification, regression, clustering, deviation detection, and association analysis; and evaluation of patterns minded from data. Industrial and scientific applications are discussed. Recommended background: Background in artificial intelligence, databases, and statistics at the undergraduate level, or permission of the instructor. Proficiency in a high level programming language.
CS 549. COMPUTER VISION

This course examines current issues in the computer implementation of visual perception. Topics include image formation, edge detection, segmentation, shapefromshading, motion, stereo, texture analysis, pattern classification and object recognition. We will discuss various representations for visual information, including sketches and intrinsic images. (Prerequisites: CS 534, CS 543, CS 545, or the equivalent of one of these courses.)
CS 557. SOFTWARE SECURITY DESIGN AND ANALYSIS

Software is responsible for enforcing many central security goals in computer systems. These goals include authenticating users and other external principals, authorizing their actions, and ensuring the integrity and confidentiality of their data. This course studies how to design, implement, and analyze mechanisms to enforce these goals in both web systems and programs in traditional languages. Topics include: identifying programming choices that lead to reliable or flawed security outcomes, successful and unsuccessful strategies for incorporating cryptography into software, and analysis techniques that identify security vulnerabilities. The course will cover both practical and theoretical aspects of secure software, and will include a substantial secure software design project. (Prerequisites: Programming and software engineering experience (commensurate with an undergraduate Computer Science major), and background in foundational models of computing systems (on par with CS 5003 or CS 503).)
CS 558. COMPUTER NETWORK SECURITY

This course covers core security threats and mitigations at the network level. Topics include: denialofservice, network capabilities, intrusion detection and prevention systems, worms, botnets, Web attacks, anonymity, honeypots, cybercrime (such as phishing), and legality and ethics. The course prepares students to think broadly and concretely about network security; it is not designed to teach students lowlevel tools for monitoring or maintaining system security. Assignments and projects will assess each student’s ability to think both conceptually and practically about network security. (Prerequisites: a strong background in computer networking and systems, either at the undergraduate or graduate level, and moderate programming experience.)
CS 561. ADVANCED TOPICS IN DATABASE SYSTEMS

This course covers modern database and information systems as well as research issues in the field. Topics and systems covered may include object oriented, workflow, active, deductive, spatial, temporal and multimedia databases. Also discussed will be recent advances in database systems such as data mining, online analytical processing, data warehousing, declarative and visual query languages, multimedia database tools, web and unstructured data sources, and clientserver and heterogeneous systems. The specific subset of topics for a given course offering is selected by the instructor. Research papers from recent journals and conferences are used. Group project required. (Prerequisites: CS 542 or equivalent. Expected background includes a knowledge of relational database systems.) See the SUPPLEMENT section of the online catalog at www.wpi.edu/+gradcat for descriptions of courses to be offered in this academic year.
CS 565. USER MODELING

User modeling is a crossdisciplinary research field that attempts to construct models of human behavior within a specific computer environment. Contrary to traditional artificial intelligence research, the goal is not to imitate human behavior as such, but to make the machine able to understand the expectations, goals, knowledge, information needs, and desires of a user in terms of a specific computing environment. The computer representation of this information about a user is called a user model, and systems that construct and utilize such models are called user modeling systems. A simple example of a user model would be an ecommerce site which makes use of the user’s and similar users’ purchasing and browsing behavior in order to better understand the user’s preferences. In this class, the focus is on obtaining a general understanding of user modeling, and an understanding of how to apply user modeling techniques. Students will read seminal papers in the user modeling literature, as well as complete a course project where students build a system that explicitly models the user. (Prerequisites: Knowledge of probability.)
CS 566. GRAPHICAL MODELS FOR REASONING UNDER UNCERTAINTY

This course will introduce students to graphical models, such as Bayesian networks, Hidden Markov Models, Kalman filters, particle filters, and structural equation models. Graphical models are applicable in a wide variety of work in computer science for reasoning under uncertainty such as user modeling, speech recognition, computer vision, object tracking, and determining a robot’s location. This course will cover 1) using data to estimate the parameters and structure of a model using techniques such as expectation maximization, 2) understanding techniques for performing efficient inference on new observations such as junction trees and sampling, and 3) learning about evaluation techniques to determine whether a particular model is a good one. (Prerequisites: CS 534 Artificial Intelligence or permission of the instructor.)
CS 567. EMPIRICAL METHODS FOR HUMANCENTERED COMPUTING

This course introduces students to techniques for performing rigorous empirical research in computer Science. Since good empirical work depends on asking good research questions, this course will emphasize creating conceptual frameworks and using them to drive research. In addition to helping students understand what makes a good research question and why, some elementary statistics will be covered. Furthermore, students will use and implement computationally intensive techniques such as randomization, bootstrapping, and permutation tests. The course also covers experiments involving human subjects, and some of the statistical and nonstatistical difficulties researchers often encounter while performing such work (e.g., IRB (Institutional Review Board), correlated trials, and small sample sizes). While this course is designed for students in Human Computer Interaction, Interactive Media and Game Development, and Learning Sciences and Technologies, it is appropriate for any student with programming experience who is doing empirical research. (Prerequisites: MA 511 Applied Statistics for Engineers and Scientists or permission of the instructor.)
CS 568. ARTIFICIAL INTELLIGENCE FOR ADAPTIVE EDUCATIONAL TECHNOLOGY

Students will learn how to enable educational technology to adapt to the user and about typical architectures used by existing intelligent tutoring systems for adapting to users. Students will see applications of decision theoretic systems, reinforcement learning, Markov models for action selection, and Artificial Intelligence (AI) planning. Students will read papers that apply AI techniques for the purpose of adapting to users. Students will complete a project that applies these techniques to build an adaptive educational system. (Prerequisites: CS 534 Artificial Intelligence or permission of the instructor.)
CS 571. CASE STUDIES IN COMPUTER SECURITY

This course examines security challenges and failures holistically, taking into account technical concerns, human behavior, and business decisions. Using a series of detailed case studies, students will explore the interplay among these dimensions in creating secure computing systems and infrastructure. Students will also apply lessons from the case studies to emerging securesystems design problems. The course requires active participation in class discussions, presentations, and writing assignments. It does not involve programming, but assumes that students have substantial prior experience with security protocols, attacks, and mitigations at the implementation level. This course satisfies the behavioral component of the MS specialization in computer security. (Prerequisites: A prior course or equivalent experience in technical aspects of computer security, at either the software or systems level.)
CS 573. DATA VISUALIZATION

This course exposes students to the field of data visualization, i.e., the graphical communication of data and information for the purposes of presentation, confirmation, and exploration. The course introduces the stages of the visualization pipeline. This includes data modeling, mapping data attributes to graphical attributes, visual display techniques, tools, paradigms, and perceptual issues. Students learn to evaluate the effectiveness of visualizations for specific data, task, and user types. Students implement visualization algorithms and undertake projects involving the use of commercial and publicdomain visualization tools. Students also read papers from the current visualization literature and do classroom presentations. Prerequisite: a graduate or undergraduate course in computer graphics.
CS 577. ADVANCED COMPUTER AND COMMUNICATIONS NETWORKS

This course covers advanced topics in the theory, design and performance of computer and communications networks. Topics will be selected from such areas as local area networks, metropolitan area networks, wide area networks, queueing models of networks, routing, flow control, new technologies and protocol standards. The current literature will be used to study new networks concepts and emerging technologies. (Prerequisite: CS 513/ECE 506 and CS 533/ECE 581).
CS 578. CRYPTOGRAPHY AND DATA SECURITY

This course gives a comprehensive introduction to the field of cryptography and data security. The course begins with the introduction of the concepts of data security, where classical algorithms serve as an example. Different attacks on cryptographic systems are classified. Some pseudorandom generators are introduced. The concepts of public and private key cryptography are developed. As important representatives for secret key schemes, DES and IDEA are described. The public key schemes RSA and ElGamal, and systems based on elliptic curves are then developed. Signature algorithms, hash functions, key distribution and identification schemes are treated as advanced topics. Some advanced mathematical algorithms for attacking cryptographic schemes are discussed. Application examples will include a protocol for security in a LAN and a secure smart card system for electronic banking. Special consideration will be given to schemes which are relevant for network environments. For all schemes, implementation aspects and uptodate security estimations will be discussed. (Prerequisites: Working knowledge of C; an interest in discrete mathematics and algorithms is highly desirable. Students interested in a further study of the underlying mathematics may register for MA 4891 [B term], where topics in modern algebra relevant to cryptography will be treated.)
CS 582. BIOVISUALIZATION

This course will use interactive visualization to model and analyze biological information, structures, and processes. Topics will include the fundamental principles, concepts, and techniques of visualization (both scientific and information visualization) and how visualization can be used to study bioinformatics data at the genomic, cellular, molecular, organism, and population levels. Students will be expected to write small to moderate programs to experiment with different visual mappings and data types. (Prerequisite: strong programming skills, an undergraduate or graduate course in algorithms, and one or more undergraduate biology courses.) Students may not receive credit for both CS 582 and CS 4802.
CS 583. BIOLOGICAL AND BIOMEDICAL DATABASE MINING

This course will investigate computational techniques for discovering patterns in and across complex biological and biomedical sources including genomic and proteomic databases, clinical databases, digital libraries of scientific articles, and ontologies. Techniques covered will be drawn from several areas including sequence mining, statistical natural language processing and text mining, and data mining. (Prerequisite: strong programming skills, an undergraduate or graduate course in algorithms, an undergraduate course in statistics, and one or more undergraduate biology courses.) Students may not receive credit for both CS 583 and CS 4803.
CS 584. ALGORITHMS: DESIGN AND ANALYSIS

This covers the same material as CS 5084 though at a more advanced level. As background, students should have experience writing programs in a recursive, highlevel language and should have the background in mathematics that could be expected from a BS in Computer Science.
CS 585. BIG DATA MANAGEMENT

Emerging applications in science and engineering disciplines generate and collect data at unprecedented speed, scale, and complexity that need to be managed and analyzed efficiently. This course introduces the emerging techniques and infrastructures developed for big data management including parallel and distributed database systems, mapreduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloudbased computing. Query processing, optimization, access methods, storage layouts, and energy management techniques developed on these infrastructures will be covered. Students are expected to engage in handson projects using one or more of these technologies. Prerequisites: A beginning course in databases at the level of CS4432 or equivalent knowledge, and programming experience.
CS 586. BIG DATA ANALYTICS

Innovation and discoveries are no longer hindered by the ability to collect data, but the ability to summarize, analyze, and discover knowledge from the collected data in a scalable fashion. This course covers computational techniques and algorithms for analyzing and mining patterns in largescale datasets. Techniques studied address data analysis issues related to data volume (scalable and distributed analysis), data velocity (highspeed data streams), data variety (complex, heterogeneous, or unstructured data), and data veracity (data uncertainty). Techniques include mining and machine learning techniques for complex data types, and scaleup and scaleout strategies that leverage big data infrastructures. Realworld applications using these techniques, for instance social media analysis and scientific data mining, are selectively discussed. Students are expected to engage in handson projects using one or more of these technologies. Prerequisites: A beginning course in databases and a beginning course in data mining, or equivalent knowledge, and programming experience.
DS 501. INTRODUCTION TO DATA SCIENCE

This course provides an overview of Data Science, covering a broad selection of key challenges in and methodologies for working with big data. Topics to be covered include data collection, integration, management, modeling, analysis, visualization, prediction and informed decision making, as well as data security and data privacy. This introductory course is integrative across the core disciplines of Data Science, including databases, data warehousing, statistics, data mining, data visualization, high performance computing, cloud computing, and business intelligence. Professional skills, such as communication, presentation, and storytelling with data, will be fostered. Students will acquire a working knowledge of data science through handson projects and case studies in a variety of business, engineering, social sciences, or life sciences domains. Issues of ethics, leadership, and teamwork are highlighted. Prerequisites:None beyond meeting the Data Science admission criteria.
SEME 565. USER MODELING

User modeling is a crossdisciplinary research field that attempts to construct models of human behavior within a specific computer environment. Contrary to traditional artificial intelligence research, the goal is not to imitate human behavior as such, but to make the machine able to understand the expectations, goals, knowledge, information needs, and desires of a user in terms of a specific computing environment. The computer representation of this information about a user is called a user model, and systems that construct and utilize such models are called user modeling systems. A simple example of a user model would be an ecommerce site which makes use of the user’s and similar users’ purchasing and browsing behavior in order to better understand the user’s preferences. In this class, the focus is on obtaining a general understanding of user modeling, and an understanding of how to apply user modeling techniques. Students will read seminal papers in the user modeling literature, as well as complete a course project where students build a system that explicitly models the user. (Prerequisites: Knowledge of probability.)
SEME 566. GRAPHICAL MODELS FOR REASONING UNDER UNCERTAINTY

This course will introduce students to graphical models, such as Bayesian networks, Hidden Markov Models, Kalman filters, particle filters, and structural equation models. Graphical models are applicable in a wide variety of work in computer science for reasoning under uncertainty such as user modeling, speech recognition, computer vision, object tracking, and determining a robot’s location. This course will cover 1) using data to estimate the parameters and structure of a model using techniques such as expectation maximization, 2) understanding techniques for performing efficient inference on new observations such as junction trees and sampling, and 3) learning about evaluation techniques to determine whether a particular model is a good one. (Prerequisites: CS 534 Artificial Intelligence or permission of the instructor.)
SEME 567. EMPIRICAL METHODS FOR HUMANCENTERED COMPUTING

This course introduces students to techniques for performing rigorous empirical research in computer Science. Since good empirical work depends on asking good research questions, this course will emphasize creating conceptual frameworks and using them to drive research. In addition to helping students understand what makes a good research question and why, some elementary statistics will be covered. Furthermore, students will use and implement computationally intensive techniques such as randomization, bootstrapping, and permutation tests. The course also covers experiments involving human subjects, and some of the statistical and nonstatistical difficulties researchers often encounter while performing such work (e.g., IRB (Institutional Review Board), correlated trials, and small sample sizes). While this course is designed for students in Human Computer Interaction, Interactive Media and Game Development, and Learning Sciences and Technologies, it is appropriate for any student with programming experience who is doing empirical research. (Prerequisites: MA 511 Applied Statistics for Engineers and Scientists or permission of the instructor.)
SEME 568. ARTIFICIAL INTELLIGENCE FOR ADAPTIVE EDUCATIONAL TECHNOLOGY

Students will learn how to enable educational technology to adapt to the user and about typical architectures used by existing intelligent tutoring systems for adapting to users. Students will see applications of decision theoretic systems, reinforcement learning, Markov models for action selection, and Artificial Intelligence (AI) planning. Students will read papers that apply AI techniques for the purpose of adapting to users. Students will complete a project that applies these techniques to build an adaptive educational system. (Prerequisites: CS 534 Artificial Intelligence or permission of the instructor.)