Course Numbers, Titles, and Credit Hours
202 Introduction to Computation 3 cr.
An introduction to the principles that form the foundation of
computer science. Suitable for students with a general background who
wish to study the key principles of computer science rather than just
computer programming.
240 Introduction to Discrete Mathematics 3 cr. (also Math)
Basic concepts of mathematics (definitions, proofs, sets, functions,
and relations) with a focus on discrete structures: integers, bits,
strings, trees, and graphs.
Propositional logic, Boolean algebra, and predicate logic.
Mathematical induction and recursion.
Invariants and algorithmic correctness.
Recurrences and asymptotic growth analysis.
Fundamentals of counting.
Prereq: Math 221.
250 Digital Society: The Impact of Computers and Computer
Technology 3 cr.
Introduction to computers in the digital society; social changes they
influence, and choices they present. Topics include: digital divide, role of
computers in improving quality of life, electronic voting and governance,
digital intellectual property rights, privacy, computers and the environment.
252 Introduction to Computer Engineering 2 cr. (also ECE)
Logic components built with transistors, rudimentary Boolean
algebra, basic combinational logic design, basic synchronous sequential logic
design, basic computer organization and design, introductory machine-and
assembly-language programming.
270 Fundamentals of Human-Computer Interaction 3 cr.
User-centered software design including principles and methods for understanding user needs, designing and prototyping interface solutions, and evaluating their usability covered through lectures, hands-on in-class activities, and weeklong assignments. Meets with CS 570.
298 Directed Study in Computer Science 1-3 cr.
Undergraduate directeed study in computer sciences.
Prereq: Open to Fr.
302 Introduction to Programming 3 cr.
Instruction and experience in the use of an
object-oriented programming language. Program design; development of
good programming style; preparation for other Computer Science
courses.
Prereq: Problem solving skills such as those acquired
in a statistics, logic, or advanced high school algebra course; or
consent of instructor. Open to Fr.
304 WES-CS Group Meeting
Small group meetings for Wisconsin Emerging Scholars--Computer Science
(WES-CS) students. Meet for two hours each week in
small groups to work together on problems related to the CS 302 course
material. Co-req: CS 302 and WES-CS membership.
Open to Fr.
Prereq: No prerequisites. Co-requisites include enrollment in CS 302 and membership in the WES-CS (WSCS) student group.
310 Problem Solving using Computers 3 cr.
Gives engineering students an introduction to computer and analytical skills to
use in their subsequent course work and professional development. Discusses
several methods of using computers to solve problems, including elementary
Fortran and C programming techniques, the use of spreadsheets, symbolic
manipulation languages, and software packages. Techniques will be illustrated
using sample problems drawn from elementary engineering. Emphasis on
introduction of algorithms with the use of specific tools to illustrate the
methods.
Prereq: Math 222.
352 Digital Systems Fundamentals 3 cr. (also ECE)
Logic components, Boolean algebra, combinational logic analysis and synthesis,
synchronous and asynchronous sequential logic analysis and design, digital
subsystems, computer organization and design.
Prereq: CS 252 or
equivalent. Not open to students with EGR classification.
354 Machine Organization and Basic Systems 3 cr.
An introduction to current system structures of control, communication,
memories, processors and I-O devices. Projects involve detailed study and use
of a specific small computer hardware and software system.
Prereq: CS 302 and ECE/CS 252 or consent of
instructor. Open to Fr.
367 Introduction to Data Structures 3 cr.
Study of data structures (including stacks,
queues, trees, graphs, and hash tables) and their applications.
Development, implementation, and analysis of efficient data structures
and algorithms (including sorting and searching). Experience in use of
an object-oriented programming language.
Prereq: CS 302 or
consent of instructor. Students are strongly encouraged to take
CS 367 within two semesters of having taken
CS 302.
368 Learning a New Programming Language 1 cr.
For students interested in learning a particular programming language.
Each 1-credit course focuses on a specific language offered at one of
three levels: beginner, intermediate, and advanced. Prereq: students
may not receive credit twice for the same language at the same level.
369 Web Programming 3 cr.
Covers web application development end-to-end: languages and frameworks for
client- and server-side programming, database access, and other topics.
Involves hands-on programming assignments. Students attain a thorough
understanding of and experience with writing web applications using tools
popular in industry.
Prereq: CS 367 or substantial programming experience and
consent of instructor.
(Not likely to be offered soon.)
371 Technology of Computer-Based Business Systems 3 cr. (also Info Sys)
Overview of computers, their attendant technology, and the implications of
this technology for large-scale, computer-based information systems. Topics
include hardware, system software, program development, files, and data
communications.
Prereq: Bus 370 and CS 302, or equivalent
experience with consent of instructor.
402 Introducing Computer Science to K-12 Students 2 cr.
Work in teams to lead Computer Science clubs and workshops for K-12
students at sites in the Madison area. Design and lead activities to
help K-12 students learn computational thinking and computer
programming.
Prereq: Any course in Computer Sciences (e.g.,
CS 202, CS 302,
CS 310).
407 Foundations of Mobile Systems and Applications 3 cr.
Design and implementation of applications, systems, and services for mobile platforms with (i) constraints, such as limited processing, memory, energy, interfaces, variable bandwidth, and high mobility, and (ii) features, such as touchscreens, cameras, electronic compasses, GPS, and accelerometers.
Prereq: CS 367.
412 Introduction to Numerical Methods 3 cr.
Interpolation, solution of linear and nonlinear systems of equations,
approximate integration and differentiation, numerical solution of ordinary
differential equations.
Prereq: Math 222 and either CS 240 or Math 234, and
CS 302, or equivalent, and knowledge of matrix algebra.
416 Foundations of Scientific Computing 3 cr.
Basic techniques for scientific computing, including fundamentals of linear
algebra and numerical linear algebra, rootfinding, floating-point arithmetic,
interpolations and splines, linear and quadratic programming.
Prereq: Math 222 and either CS 240 or Math 234, and
CS 302, or equivalent.
425 Introduction to Combinatorial Optimization 3 cr. (also Math & ISyE)
Exact and heuristic methods for key combinatorial optimization problems such
as: shortest path, maximum flow problems, and the traveling salesman problem.
Techniques include problem-specific methods and general approaches such as
branch-and-bound, genetic algorithms, simulated annealing, and neural
networks.
Prereq: Math 221 or CS 302 or consent of instructor.
435 Introduction to Cryptography 3 cr. (also Math &
ECE)
Cryptography is the art and science of transmitting digital information in a
secure manner. This course will provide an introduction to its technical
aspects.
Prereq: Math 320 or 340 or consent of instructor.
471 Introduction to Computational Statistics 3 cr. (also Stat)
An introduction to computer-simulation-based statistical inference and
estimation. Generating random numbers; Monte Carlo integration;
Importance Sampling; Bootstrap; cross-validation; model selection;
expectation maximization algorithm; jackknife; Markov Chains; Markov
Chain Monte Carlo; Metropolis-Hastings algorithms; Gibbs sampler.
Prereq: Stat/Math 309-310 or Stat 311-312 or consent of instructor.
475 Introduction to Combinatorics 3 cr. (also Math & Stat)
Problems of enumeration, distribution and arrangement. Inclusion-exclusion
principle. Generating functions and linear recurrence relations.
Combinatorial identities. Graph coloring problems. Finite designs. Systems
of distinct representatives and matching problems in graphs. Potential
applications in the social, biological, and physical sciences. Puzzles.
Emphasis on problem solving.
Prereq: Math 320 or 340 and consent of instructor.
506 Software Engineering 3 cr.
Ideas and techniques for designing, developing, and modifying large
software systems. Topics include software engineering processes;
requirements and specifications; project team organization and
management; software architectures; design patterns; testing and
debugging; and cost and quality metrics and estimation. Students will
work in large teams on a substantial programming project.
Prereq: CS 367 and at least one of
CS 407, CS 536, CS 537, CS 545, CS 552, CS 559, CS 564, CS 570, or CS 679.
513 Numerical Linear Algebra 3 cr. (also Math)
Direct and iterative solution of linear and nonlinear systems and of
eigenproblems. LU and symmetric LU factorization. Complexity, stability, and
conditioning. Nonlinear systems. Iterative methods for linear
systems. QR-factorization and least squares. Eigenproblems: local and global
methods.
Prereq: Math 340 or equivalent; CS 302 or equivalent.
514 Numerical Analysis 3 cr. (also Math)
Polynomial forms, divided differences. Polynomial interpolation. Polynomial
approximation: uniform approximation and Chebyshev polynomials, least-squares
approximation and orthogonal polynomials. Splines, B-splines and spline
approximation. Numerical differentiation and integration. Numerical methods
for solving initial and boundary value problems for ordinary differential
equations.
Prereq: Math 340 or equivalent; CS 302 or equivalent.
515 Introduction to Splines and Wavelets 3 cr. (also Math)
Introduction to Fourier series and Fourier transform; time-frequency
localization; wavelets and frames; applications: denoising and compression of
signals and images. Interpolation and approximation by splines:
interpolation, least-squares approximation, smoothing, knot insertion and
subdivision; splines in CAGD.
Prereq: Math 340 or equivalent; CS 302 or equivalent.
520 Introduction to Theory of Computing 3 cr.
Basics about the notion, capabilities, and limitations of computation:
elements of finite automata and regular languages, computability
theory, and computational complexity theory. Additional topics include
context-free grammars and languages, and complexity-theoretic
cryptography.
Prereq: CS 240, and CS 367, or
consent of instructor.
525 Linear Programming Methods 3 cr.
Real linear algebra over polyhedral cones, theorems of the alternative for
matrices. Formulation of linear programs. Duality theory and solvability.
The simplex method and related methods for efficient computer solution.
Perturbation and sensitivity analysis. Applications and extensions, such as
game theory, linear economic models and quadratic programming.
Prereq: Math 443 or 320 or 340 or consent of instructor.
526 Advanced Linear Programming 4 cr. ugrad, 3 cr. grad (also ISyE)
Review of linear programming. Polynomial time methods for linear
programming. Quadratic programs and linear complementarity problems and
related solution techniques. Solution sets and their continuity
properties. Error bounds for linear inequalities and programs. Parallel
algorithms for linear and quadratic programs.
Prereq: CS 525 or equivalent,
CS 302 or equivalent, or consent of instructor.
(Not likely to be offered soon.)
532 Theory and Applications of Pattern Recognition 3 cr.
(also ECE
& ME)
Pattern recognition systems and components; decision theories and
classification; discriminant functions; supervised and unsupervised training;
clustering; feature extraction and dimensional reduction; sequential and
hierarchical classification; applications of training, feature extraction, and
decision rules to engineering problems.
Prereq: ECE 331 or Math 431 or consent of instructor.
533 Image Processing 3 cr.
(also ECE)
Mathematical representation of continuous and digital images; models of image
degradation; picture enhancement, restoration, segmentation, and coding;
pattern recognition, tomography.
Prereq: ECE 330 or consent of instructor; Math 320 or 340 or equiv. recommended.
534 Computational Photography 3 cr.
Study of sensing and computational techniques that enhance or extend
the capabilities of digital photography by using methods from computer
vision and computer graphics to create new visual representations.
Algorithms for analyzing, improving, manipulating, combining, and
synthesizing images.
Prereq: CS 367.
536 Introduction to Programming Languages and Compilers 3 cr.
Introduction to the theory and practice of compiler design. Comparison of
features of several programming languages and their implications for
implementation techniques. Several programming projects required.
Prereq: CS 367 and either CS 354 or 552.
537 Introduction to Operating Systems 4 cr. ugrad, 3 cr. grad.
Input-output hardware, interrupt handling, properties of magnetic tapes, discs
and drums, associative memories and virtual address translation techniques.
Batch processing, time sharing and real-time systems, scheduling resource
allocation, modular software systems, performance measurement and system
evaluation.
Prereq: CS 354 and CS 367.
538 Introduction to the Theory and Design of Programming Languages 3 cr.
Design and theory of programming languages: procedural, object-oriented,
functional and logic paradigms. Serial and concurrent programming. Execution
models and formal specification techniques.
Prereq: CS 354 and CS 367.
(Not likely to be offered soon.)
539 Introduction to Artificial Neural
Networks and Fuzzy Systems 3 cr. (also ECE & ME)
Theory and applications of artificial neural networks and fuzzy logic:
multi-layer perceptrons, self-organizing maps, radial basis networks,
Hopfield networks, recurrent networks, fuzzy-set theory, fuzzy logic
control, adaptive fuzzy neural networks, genetic algorithms, and
evolutionary computing. Applications to control, pattern recognition,
nonlinear system modeling, speech and image processing.
Prereq: CS 302, or CS 310, or knowledge of C.
540 Introduction to Artificial Intelligence 3 cr.
Principles of knowledge-based search techniques; automatic deduction,
knowledge representation using predicate logic, machine learning,
probabilistic reasoning. Applications in tasks such as problem solving, data
mining, game playing, natural language understanding, computer vision, speech
recognition, and robotics.
Prereq: CS 367.
545 Natural Language and the Computer 3 cr.
The course covers basic techniques and tools in natural language processing:
generative grammars, parsing, dictionary construction, semantic networks,
generation of text from a knowledge base, natural language interfaces, and
machine translation.
Prereq: CS 536 or CS 537
or 564 or consent of instructor.
547 Computer Systems Modeling Fundamentals 3 cr.
An introduction to basic tools and applications for modeling and analysis of
computer systems. Fundamentals of network flow graphs, graph models of
computation and stochastic models of computer system performance. Network
delay analysis and capacity planning, reachability analysis for deadlock
detection in distributed systems, Markov chains, elementary queueing theory,
basic concepts of queueing network models and associated analyses.
Prereq: Math 234, CS 367 and CS 354.
552 Introduction to Computer Architecture 3 cr.
The design of computer systems and components. Processor design, instruction
set design, and addressing; control structures and microprogramming; memory
management, caches, and memory hierarchies; interrupts and I/O structures.
Prereq: ECE/CS 352 and CS/ECE 354;
co-req: CS 367.
558 Introduction to Computational Geometry 3 cr.
Introduction to fundamental geometric computations and algorithms, and their
use for solving engineering and scientific problems. Computer representations
of simple geometric objects and paradigms for algorithm design. Applications
from areas of engineering analysis, design and manufacturing, biology,
statistics, and other sciences.
Prereq: CS 367 or equivalent,
Math 234 or equivalent, or consent of instructor.
559 Computer Graphics 3 cr.
Survey of computer graphics. Image representation, formation, presentation,
composition and manipulation. Modeling, transformation, and display of
geometric objects in 2 and 3 dimensions. Representation of curves and
surfaces. Rendering, animation, multi-media and visualization.
Prereq: Math 320 or 340 (linear algebra), and CS 367.
564 Database Management Systems: Design and Implementation 4 cr. ugrad, 3 cr. grad.
What a database management system is; different data models currently used to
structure the logical view of the database: relational, hierarchical, and
network. Hands-on experience with relational and network-based database
systems. Implementation techniques for database systems. File organization,
query processing, concurrency control, rollback and recovery, integrity and
consistency, and view implementation.
Prereq: CS 367
and 354.
570 Introduction to Human-Computer Interaction 3 cr.
User-centered software design; (1) principles of and methods for understanding user needs, designing and prototyping interface solutions, and evaluating their usability, (2) their applications in designing web-based, mobile, and embodied interfaces through monthlong group projects.
Meets with CS 270.
Prereq: CS 202 or CS 302.
576 Introduction to Bioinformatics 3 cr. (also BMI)
Algorithms for computational problems in molecular biology. The course will
study algorithms for problems such as: genome sequencing and mapping,
pairwise and multiple sequence alignment, modeling sequence classes and
features, phylogenetic tree construction, and gene-expression data
analysis.
Prereq: CS 367 and Math 222.
577 Introduction to Algorithms 3 cr.
Basic paradigms for the design and analysis of efficient
algorithms: greed, divide-and-conquer, dynamic programming,
reductions, and the use of randomness. Computational
intractability including typical NP-complete problems and
ways to deal with them.
Prereq: CS 240
and CS 367, or consent of instructor.
578 Contest-Level Programming 1 cr.
Training in computer programming for competitions: assessing the
coding difficulty and complexity of computational problems,
recognizing the applicability of known algorithms, fast coding and
testing, team work.
Prereq: CS 367 is required. CS 577
is suggested but not required.
635 Tools and Environments for Optimization 3 cr.
Formulation and modeling of applications
from computer sciences, operations research, business, science and
engineering involving optimization and equilibrium models.
Survey and appropriate usage of software tools for solving such problems,
including modeling language use, automatic differentiation, subroutine
libraries and web-based optimization tools and environments.
Prereq: CS 302, Math 340 or equivalent.
638 Undergraduate Topics in Computing 3 cr.
Prereq: Consent of instructor.
640 Introduction to Computer Networks 3 cr.
Architecture of computer networks and network protocols, protocol
layering, reliable transmission, congestion control, flow
control, naming and addressing, unicast and multicast routing, network
security, network performance, widely used protocols such as Ethernet,
wireless LANs, IP, and HTTP.
Prereq: CS 537.
642 Introduction to Information Security 3 cr.
This is a senior level undergraduate course covering various topics on
information security. The course will cover a wide range of topics, such as,
cryptographic primitives, security protocols, system security, and emerging
topics.
Prereq: CS 537 or consent of instructor. Elementary
knowledge of mathematical logic and discrete probability theory is also
required.
679 Computer Game Technology 3 cr.
Survey of software technology important to computer games and other forms
of interactive technology: Real-time image generation, managing complex
geometric models, creating virtual characters, simulating physical
phenomenon, networking technology for distributed virtual environments.
Prereq: CS 559.
681-682 Senior Honors Thesis 3 cr. per sem.
Prereq: Honors candidacy and consent of instructor.
691-692 Senior Thesis 2-3 cr. per sem.
(A year's course must be taken to get credit.)
Prereq: Consent of instructor.
699 Directed Study 1-6 cr.
Prereq: Junior or senior standing and consent of instructor.
701 Programming Languages and Compilers 3 cr.
Design and implementation of compilers for modern programming languages.
Emphasis on tools for compiler construction.
Prereq: CS 536.
703 Advanced Topics in Programming Languages and Compilers 3 cr.
Advanced topics in compiling and programming languages design. Advanced
parsing techniques; automatic syntactic error correction; local and global
code optimization; attribute grammars; programming language design issues
(data and control abstractions, specification and verification of high level
languages).
Prereq: CS 701.
(Not likely to be offered soon.)
704 Principles of Programming Languages 3 cr.
Introduction to principles of advanced programming languages and
programming-language theory. Topics include: lambda-calculus, functional
languages, polymorphic functions, type inference, structural induction, lazy
evaluation, operational semantics, denotational semantics, and axiomatic
semantics.
Prereq: CS 536 or consent of instructor.
706 Analysis of Software Artifacts 3 cr.
Advanced course covering various analysis techniques used in software
engineering. This course will cover techniques for analyzing various software
artifacts. Some of the topics that will be covered are: model checking,
testing, program analysis, requirements analysis, and safety analysis.
Prereq: CS 536 or consent of instructor. A basic knowledge
of mathematical logic is also required.
707 Mobile and Wireless Networking 3 cr. (also ECE)
Design and implementation of protocols, systems, and applications for mobile
and wireless networking, particularly at the media access control, network,
transport, and application layers. Focus is on the unique problems and
challenges presented by the properties of wireless transmission, various
device constraints such as limited battery power, and node mobility.
Prereq: CS 640 or CS 537 or equivalent, or
permission of the instructor.
710 Computational Complexity 3 cr.
Study of the capabilities and limitations of efficient computation.
Relationships between models representing capabilities such as
parallelism, randomness, quantum effects, and non-uniformity; and
models based on the notions of nondeterminism, alternation, and
counting, which capture the complexity of important problems.
Prereq: CS 520.
714 Methods of Computational Mathematics I 3 cr. (also Math)
Development of finite difference methods for hyperbolic, parabolic, and
elliptic partial differential equations. Analysis of accuracy and stability of
difference schemes. Direct and iterative methods for solving linear systems.
Introduction to finite volume methods. Applications from science
and engineering.
Prereq: CS 302,
CS 412, Math 322, 340, 521 or equivalent, or consent of
instructor.
715 Methods of Computational Mathematics II 3 cr. (also Math)
Introduction to spectral methods (Fourier; Chebyshev; Fast Fourier Transform),
finite element methods (Galerkin methods; energy estimates and error analysis),
and mesh-free methods (Monte Carlo; smoothed-particle hydrodynamics) for
solving partial differential equations. Applications from science and
engineering.
Prereq: CS 302,
CS 412, Math 322, 340, 521 or equivalent, or consent of
instructor.
717 Numerical Functional Analysis 3 cr.
Fundamentals of normed spaces and linear operators; analysis of nonlinear
operators; existence of, and iterative methods for, solutions of linear and
nonlinear operator equations, error estimation; variational theory and
minimization problems; monotonicity theory. Development of abstract tools and
application of them to the general analysis of numerical methods for such
problems as differential and integral equations.
Prereq: CS 513, CS 514 and Math 234
or consent of instructor.
(Not likely to be offered soon.)
719 Stochastic Programming 3 cr.
Stochastic programming is concerned with decision making in the
presence of uncertainty, where the eventual outcome depends on a
future random event. Topics include modeling uncertainty in
optimization problems, risk measures, stochastic programming
algorithms, approximation and sampling methods, and applications.
Prereq: CS 525 or consent of instructor.
720 Integer Programming 3 cr. (also ISyE)
Formulation of integer programming problems and the characterization of
optimization problems representable as integer and mixed-integer programs.
The degree of difficulty of classes of integer programs and its relation to
the structure of their feasible sets. Optimality conditions.
Branch-and-bound, cutting plane, and decomposition methods for obtaining
solutions or approximating solutions.
Prereq: CS 525 or consent of instructor.
723 Dynamic Programming and Associated Topics 3 cr. (also ISyE)
A generalized optimization model; discrete and continuous state spaces;
deterministic and stochastic transition functions. Multistage decision
processes. Functional equations and successive approximation in function and
policy spaces. Relationship to linear programming and acyclic networks.
Markovian decision processes. Solution methods and computational problems.
Associated topics and applications such as calculus of variations; feedback
control processes; and optimal trajectories, inventory and maintenance
policies, and stopping rules.
Prereq: CS 525 or ISyE 623;
Math 521 or CS 726; Math 431 and computer programming, or
consent of instructor.
726 Nonlinear Optimzation I 3 cr. (also Math, ISyE & Stat)
Theory and algorithms for nonlinear optimization, focusing on
unconstrained optimization. Line-search and trust-region methods;
quasi-Newton methods; conjugate-gradient and limited-memory methods
for large-scale problems; derivative-free optimization; algorithms for
least-squares problems and nonlinear equations; gradient projection
algorithms for bound-constrained problems; and simple penalty methods
for nonlinearly constrained optimization.
Prereq: Familiarity with basic mathematical analysis (e.g., Math 521) and
either Math. 443 or 320, or consent of instructor.
727 Advanced Nonlinear Programming 3 cr. (also ISyE)
Conjugate convex functions and Fenchel-Rockafellar duality. Monotone operators
and subdifferentials. Advanced methods for nonconvex problems, such as
variational principles, generalized gradients, degree and index arguments, and
multivalued ordinary differential equations. Applications to economics and
operations research.
Prereq: CS 726 or consent of instructor.
730 Nonlinear Optimization II 3 cr. (also ISyE)
Theory and algorithms for nonlinearly constrained
optimization. Relevant geometric concepts, including tangent and
normal cones, theorems of the alternative, and separation
results. Constraint qualifications. Geometric and algebraic expression
of first-order optimality conditions. Second-order optimality
conditions. Duality. Nonlinear programming algorithms: Merit functions
and filters; interior-point, augmented Lagrangian, and sequential
quadratic programming algorithms.
Prereq: CS 726 or equivalent or consent of instructor.
731 Advanced Artificial Intelligence 3 cr.
Learning and hypothesis formation; knowledge acquisition; deductive and
inductive inference systems; reasoning techniques involving time, nonmonotonic
reasoning, spatial reasoning, truth maintenance systems; planning strategies.
Prereq: CS 540.
733 Computational Methods for Large Sparse Systems 3 cr. (also Math & ECE)
Sparse matrices in engineering and science. Sparsity preservation. Numerical
error control. Transversal algorithms, Tarjan's algorithm, Tinney's
algorithms, minimum degree, banding, nested dissection, frontal methods.
Linear and nonlinear equation solving. Compensation. Sparse vector methods.
Iterative methods. ODE and PDE applications.
Prereq: CS 367
and (ECE 334 or ( CS 412 and Math 340)); or consent of
instructor.
(Not likely to be offered soon.)
736 Advanced Operating Systems 3 cr.
Advanced topics in operating systems, including process communication,
resource allocation, multiprocess and network operating systems, kernel
philosophies, fault-tolerant systems, virtual machines, high-level language
systems, verifiability and proof techniques.
Prereq: CS 537 or consent of instructor.
737 Computer System Performance Evaluation and Modeling 3 cr.
Statistical techniques of computer system performance evaluation and
measurement. System selection and tuning strategies. Deterministic and
probabilistic models of process scheduling and resource allocation. Analytic
and simulation models of computer systems. Systematic study of system
architectures.
Prereq: Math 222, CS 537 or CS 736, or
consent of instructor.
739 Distributed Systems 3 cr.
Basic concepts, distributed programming; distributed file systems; atomic
actions; fault tolerance, transactions, program & data replication, recovery;
distributed machine architectures; security and authentication; load balancing
and process migration; distributed debugging; distributed performance
measurement; distributed simulation techniques; distributed applications;
correctness considerations and proof systems.
Prereq: CS 736 or consent of instructor.
740 Advanced Computer Networks 3 cr.
Advanced topics in computer communications networks: Congestion and flow
control; Routing; Rate-based protocols; High-speed interfaces and
technologies; Metropolitan area networks; Fast packet switching technologies;
Advanced applications; Network services: name service, authentication,
resource location.
Prereq: CS 640.
747 Advanced Computer Systems Analysis Techniques 3 cr.
Advanced analytical modeling techniques for performance analysis of computer
systems, including discrete-parameter (embedded) Markov Chains, M/G/1 queues,
stochastic Petri nets, queueing networks, renewal theory, and sample path
analysis. Application areas include high performance computer architectures,
databases, and operating system resource allocation policies.
Prereq: CS 547 or consent of instructor.
750 Real-Time Computing Systems 3cr.
(also ECE)
Introduction to the unique issues in the design and analysis of computer
systems for real-time applications. Hardware and software support for
guaranteeing timeliness with and without failures. Resource management,
time-constrained communication, scheduling and imprecise computations,
real-time kernels and case studies.
Prereq: CS 552 and 537 or consent of
instructor.
752 Advanced Computer Architecture 3 cr.
Advanced techniques of computer design. Parallel processing and pipelining;
multiprocessors, multi-computers and networks; high performance machines and
special purpose processors; data flow architecture.
Prereq: ECE/CS 552 and CS 537.
755 VLSI Systems Design 3 cr.
Overview of MOS devices and circuits; introduction to integrated circuit
fabrication; topological design of data flow and control; interactive graphics
layout; circuit simulation; system timing; organizational and architectural
considerations; alternative implementation approaches; design project.
Prereq: ECE 340, ECE/CS 352, and CS/ECE 552
or consent of instructor.
756 Computer-Aided Design for VLSI 3 cr.
Broad introduction to computer-aided design tools for VLSI, emphasizing
implementation algorithms and data structures. Topics covered: design styles,
layout editors, symbolic compaction, module generators, placement and routing,
automatic synthesis, design-rule checking, circuit extraction, simulation and
verification.
Prereq: CS 367, good programming skills,
CS 352; CS 755 strongly recommended.
757 Advanced Computer Architecture 3 cr.
Parallel algorithms, principles of parallelism detection and vectorizing
compilers, interconnection networks, SIMD/MIMD machines, processor
synchronization, data coherence, multis, dataflow machines, special purpose
processors.
Prereq: CS 752 or consent of instructor.
758 Advanced Topics in Computer Architecture 3 cr.
Advanced topics in computer architecture that explore the implications to architecture of
forthcoming evolutionary and revolutionary changes in application demands,
software paradigms, and hardware implementation technologies.
Prereq: CS 752 and CS/ECE 757 required. Alternatively, consent of instructor.
760 Machine Learning 3 cr.
Computational approaches to learning: including inductive inference,
explanation-based learning, analogical learning, connectionism, and formal
models. What it means to learn. Algorithms for learning. Comparison and
evaluation of learning algorithms. Cognitive modeling and relevant
psychological results.
Prereq: CS 540.
761 Advanced Machine Learning 3 cr.
Advanced computational approaches to learning. Quantification of
learnability and rate of learning, probabilistic and other formalisms
of learning, statistical and computational analysis of learning
models, state-of-the-art learning algorithms.
Prereq: CS 760 or consent of instructor.
764 Topics in Database Management Systems 3 cr.
Implementation of database management systems, the impact of new technology on
database management systems, back-end database computers, distributed database
management systems, concurrency control and query execution in both
distributed and centralized systems, implementation of multiple user views,
roll-back and recovery mechanisms, database translation.
Prereq: CS 564, CS 537,
and CS 536 or consent of instructor.
766 Computer Vision 3 cr.
Fundamentals of image analysis and computer vision; image acquisition and
geometry; image enhancement; recovery of physical scene characteristics;
shape-from techniques; segmentation and perceptual organization;
representation and description of two-dimensional and three-dimensional
objects; shape analysis; texture analysis; goal-directed and model-based
systems; parallel algorithms and special-purpose architectures.
Prereq: CS 540.
767 Computational Methods for Medical Image Analysis 3 cr. (also BMI)
Study of computational techniques that facilitate automated analysis, manipulation, denoising, and improvement of large-scale and high resolution medical images. Design and implementation of methods from Computer Vision and Machine Learning to efficiently process such image data to answer biologically and clinically meaningful scientific questions.
Prereq: CS 367 or consent of instructor.
769 Advanced Natural Language Processing 3 cr.
Develop algorithms and mathematical models for natural language processing
tasks, including text categorization, information retrieval, speech
recognition, machine translation, and information extraction. Focus is on the
state-of-the-art computational techniques as they are applied to natural
language tasks.
Prereq: CS 540 or the equivalent.
770 Human-Computer Interaction 3 cr. (also Psych)
Principles of human-computer interaction (HCI); human subjects
research methods and procedures, qualitative and quantitative data
analysis; and semester-long research project situated in critical
domains of HCI including web-based, desktop, mobile, speech-based, and
embodied interaction with computers.
776 Advanced Bioinformatics 3 cr.
Advanced course covering computational problems in molecular biology.
The course will
study algorithms for problems such as:
modeling sequence classes and features,
phylogenetic tree construction,
gene-expression data protein and RNA structure prediction, and whole-genome analysis and
comparisons.
Prereq: CS 576.
777 Computer Animation 3 cr.
Survey of technical issues in the creation of moving and dynamic computer
imagery. Principles of animation. Manual motion specification and
keyframing. Procedural and simulation-based motion synthesis. Motion
capture processing, editing and use. Animation systems. Modeling,
rendering and video issues relating to animation. Image-based animation
methods and warping. Applications of animation such as games and virtual
environments. Basic introduction to artistic issues in animation, such as
cinematography. Special Effects for Film and Video.
Prereq: CS 559.
779 Rendering Images with Computers 3 cr.
Survey of models and algorithms used in the computer generation of images.
The physics of global illumination, the global illumination equation,
approximations and techniques for solving them. Large database rendering.
Image-based methods. Stylized rendering. Point-based (splatting)
algorithms.
Prereq: CS 559.
(Not likely to be offered soon.)
784 Data Models and Languages 3 cr.
Study of database programming languages. Topics include: Logic based
languages, embedded query languages, object-oriented languages. There will be
coverage of types, persistence, inheritance, object identity, data models,
implementation issues, and case studies of actual systems and
languages.
Prereq: CS 564 and CS 536 or consent
of instructor.
Advanced paradigms for the design and analysis of efficient
algorithms, including the use of randomness, linear programming, and
semi-definite programming. Applications to data structures,
approximating NP-hard optimization problems, learning, on-line and
distributed problems.
Prereq: CS 577.
790 Master's Thesis 1-9 cr.
For students writing a Master's thesis or project.
Prereq: Master's candidate.
799 Master's Research 1-9 cr.
For pre-Master's students doing research projects.
Prereq: Master's candidate.
809 Mathematical Techniques for Analysis of Algorithms 3 cr.
Techniques for quantitative analysis of algorithms. Charging arguments,
amortization, probabilistic methods. Adversary and information lower bounds.
Use of methods from combinatorics, complex analysis, and asymptotics in
obtaining precise analyses of quicksort, chained hashing, and other
algorithms.
Prereq: CS 577, Math 321 or equivalent, and Math 431
or equivalent.
812 Arithmetic Algorithms 3 cr.
Survey of algorithms and design paradigms for exact arithmetic, as
used in public-key cryptography, computer algebra, and pseudo-random
number generation. Topics include primality testing, factorization of
integers and polynomials, discrete logarithms, and (optionally)
elliptic curves and integer lattices.
Prereq: Math 541 and CS 367,
or consent of instructor.
830 Randomness in Computation 3 cr.
Survey of uses of randomness in computer science, including
algorithms, complexity, and cryptography. Techniques for randomness
extraction, pseudo-random generator constructions, and
derandomization.
Prereq: CS 520 or CS 577.
(Not likely to be offered soon.)
837 Topics in Numerical Analysis 3 cr. (also Math)
Topic selected from advanced areas. A variable content course which may be
repeated any number of times for credit.
Prereq: Consent of instructor.
838 Topics in Computing 3 cr.
Topics selected from advanced areas. A variable content course which may be
repeated any number of times for credit.
Prereq: Consent of instructor.
841 Computational Cognitive Science 3 cr. (also BMI & Psych)
An interdisciplinary class contrasting machine learning with learning in biological systems,
particularly children. Plausible models of learning. Childhood acquisition of knowledge.
Occam's Razor, the Bayesian Occam's Razor, and PAC Learning. Frequentist vs. Bayesian Inference.
Kolmogorov Complexity. Hypothesis formation and evaluation. Learning from the real-world
without labels. Innate vs. Acquired Knowledge. Acquisition of Language.
Human rationality and Prospect Theory.
Prereq: CS 540 or consent of instructor.
880 Topics in Theoretical Computer Science 3 cr.
Advanced topics in algorithms, complexity, and cryptography. The
exact topic varies.
Prereq: consent of instructor.
887 Approximation Theory 3 cr. (also Math)
Interpolation and approximation by means of interpolation; uniform
approximation; best approximation; approximation in normed linear spaces;
spline functions; orthogonal polynomials; degree of approximation;
computational procedures.
Prereq: Consent of instructor.
899 Pre-Dissertator Research 1-9 cr.
Prereq: Post-Master's, pre-dissertator status.
915 Computation and Informatics in Biology and Medicine 1 cr. (also BMI, Gen, Biochem, CBE, and BME)
This seminar course brings together trainees, trainers, and other interested faculty
and students for cross-disciplinary exposure to current research in computer
science, biostatistics, engineering, biological sciences, and biomedical
research problems related to bioinformatics and computational biology.
Prereq: Consent of instructor.
990 Dissertation 1-6 cr.
Prereq: Dissertator status.
999 Independent Study and Research 1-6 cr.
Prereq: Dissertator status.
The nine research areas in the Department each run an advanced, non-credit
seminar where graduate students, visitors, and faculty members from within and
outside the Department present their latest research or discuss recently
published papers. These seminars give graduate students the opportunity to
learn about current research problems and to get valuable feedback on their
own research.
Also, each year the Department runs a Distinguished Lecturer Series where 6-8
leading researchers in a subfield of computer science visit.
The visitors give two lectures - one to a general computer science audience
and a second, more specialized, talk targeted toward researchers in the given