今天没吃药 感觉自己萌萌哒~~
链接：https://www.fylstudio.online/2021/04/02/[CS-List]WISC%E8%AF%BE%E7%A8%8B%E6%B8%85%E5%8D%95/
[CS-List] WISC COMPUTER SCIENCES (COMP SCI)(University of Wisconsin–Madison)-
https://shimo.im/docs/6oFRwSurzToqHQTt/
COMPUTER SCIENCES (COMP SCI)
COMP SCI 200 — PROGRAMMING I
3 credits.
Learn the process of incrementally developing small (200-500 lines)
programs along with the fundamental Computer Science topics. These
topics include: problem abstraction and decomposition, the
edit-compile-run cycle, using variables of primitive and more complex
data types, conditional and loop-based flow control, basic testing and
debugging techniques, how to define and call functions (methods), and
IO processing techniques. Also teaches and reinforces good programming
practices including the use of a consistent style, and meaningful
documentation. Intended for students who have no prior programming
experience. Enroll Info: None
Requisites: Satisfied Quantitative Reasoning (QR) A requirement
Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth
- Natural Science
Level - Elementary
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 202 — INTRODUCTION TO COMPUTATION
3 credits.
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. Enroll Info: None
Requisites: MATH 096 or placement into MATH 141. MATH 118 does not
fulfill the prerequisite. Not open to students with credit for COMP
SCI 300 or 367
Course Designation: Gen Ed - Quantitative Reasoning Part A Breadth
- Natural Science
Level - Elementary
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Fall 2018
COMP SCI 220 — DATA SCIENCE PROGRAMMING I
4 credits.
Introduction to Data Science programming using Python. No previous
programming experience required. Emphasis on analyzing real datasets
in a variety of forms and visual communication. Enroll Info: None
Requisites: Satisfied Quantitative Reasoning (QR) A requirement or
declared in the Professional Capstone Program in Computer Sciences.
Not open to students with credit for COMP SCI 301.
Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth
- Natural Science
Level - Elementary
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
Computer Sciences (COMP SCI) 1
COMP SCI/MATH 240 — INTRODUCTION TO DISCRETE MATHEMATICS 3
credits.
Basic concepts of logic, sets, partial order and other relations, and
functions. 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. Enroll Info:
None
Requisites: MATH 217, 221, or 275
Course Designation: Breadth - Natural Science
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/E C E 252 — INTRODUCTION TO COMPUTER ENGINEERING 2
credits.
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. Enroll Info: None Requisites:
None
Course Designation: Level - Elementary
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 270 — FUNDAMENTALS OF HUMAN-COMPUTER INTERACTION
3 credits.
User-centered software design including principles and methods for
understanding user needs, designing and prototyping interface
solutions, and evaluating their usability covered through lectures and
hands-on in class activities. Meets with COMP SCI 570. Enroll Info:
None Requisites: Not open to students with credit for COMP SCI 570
Course Designation: Level - Elementary
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2018
COMP SCI 298 — DIRECTED STUDY IN COMPUTER SCIENCE 1-3 credits.
Undergraduate directed study in computer sciences. Enroll Info: None
Requisites: Consent of instructor
Course Designation: Level - Elementary
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Fall 2020
2 Computer Sciences (COMP SCI)
COMP SCI 300 — PROGRAMMING II
3 credits.
Introduction to Object-Oriented Programming using classes and objects to
solve more complex problems. Introduces array-based and linked data
structures: including lists, stacks, and queues. Programming assignments
require writing and developing multi-class (file) programs using
interfaces, generics, and exception handling to solve challenging real
world problems. Topics reviewed include reading/writing data and objects
from/to files and exception handling, and command line arguments. Topics
introduced: object-oriented design; class vs. object; create and define
interfaces and iterators; searching and sorting; abstract data types
(List,Stack,Queue,PriorityQueue(Heap),Binary Search Tree); generic
interfaces (parametric polymorphism); how to design and write test
methods and classes; array based vs. linked node implementations;
introduction to complexity analysis; recursion. Enroll Info: None
Requisites: Satisfied QR-A requirement and (COMP SCI 200, 220, 302,
310, or 301) or (E C E/COMP SCI 252 and E C E 203); graduate/
professional standing; or declared in the Capstone Certificate in
Computer Sciences. Not open to students with credit for COMP SCI 367.
Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth -
Natural Science
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 304 — WES-CS GROUP MEETING
0-1 credits.
Small group meetings for Wisconsin Emerging Scholars - Computer
Science (WES-CS) students. Meets in small groups to work together on
problems related to the COMP SCI 200 course material. For information
about WES-CS membership, contact the computer sciences department.
Enroll Info: None
Requisites: Concurrent enrollment in COMP SCI 200 and member of
Wisconsin Emerging Scholars program in Computer Science Course
Designation: Level - Elementary
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 310 — PROBLEM SOLVING USING COMPUTERS 3 credits.
Gives 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 programming techniques, symbolic manipulation
languages, and software packages. Techniques will be illustrated using
sample problems drawn from elementary engineering. Emphasis is on
introduction of algorithms with the use of specific tools to
illustrate the methods. Enroll Info: None
Requisites: MATH 222, graduate/professional standing, or declared
in the Capstone Certificate in Computer Sciences for Professionals
Course Designation: Level - Elementary
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 319 — DATA PROGRAMMING I FOR RESEARCH 3 credits.
Introduction to Data Science programming using Python. In addition to
a survey of programming basics (control flow and data structures), web
scraping, database queries, and tabular analysis will be introduced.
Projects will emphasize analyzing real datasets in a variety of forms
and visual communication using plotting tools. Similar to COMP SCI 220
but the pedagogical style of the projects will be adapted to graduate
students in fields other than computer science and data science. No
previous programming experience required. Enroll Info: None
Requisites: Graduate/professional standing
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 320 — DATA PROGRAMMING II
4 credits.
Intermediate approach to Data Science programming using Python.
Experience with basic tabular analysis in Python is assumed. Learn to
implement data structures (e.g., graphs) to efficiently represent
datasets. Software-engineering tools such as version control and
Python virtual environments will be introduced, with an emphasis on
reproducibility of analysis. Tracing and A/B testing will be
introduced as techniques for generating meaningful datasets.
Introduces basic classification, clustering, optimization, and
simulation techniques. Plotting and visual communication will be
emphasized throughout the course. Recommended for Data Science majors
and other majors. Enroll Info: None
Requisites: COMP SCI 220 (or COMP SCI 301 prior to Spring 2020) or
graduate/professional standing or declared in the Professional
Capstone Program in Computer Sciences
Course Designation: Breadth - Natural Science
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/E C E 352 — DIGITAL SYSTEM FUNDAMENTALS 3 credits.
Logic components, Boolean algebra, combinational logic analysis and
synthesis, synchronous and asynchronous sequential logic analysis and
design, digital subsystems, computer organization and design. Enroll
Info: None
Requisites: Satisfied Quantitative Reasoning (QR) A requirement
and E C E/COMP SCI 252
Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth
- Physical Sci. Counts toward the Natural Sci req Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/E C E 354 — MACHINE ORGANIZATION AND PROGRAMMING 3
credits.
An introduction to fundamental structures of computer systems and the C
programming language with a focus on the low-level interrelationships
and impacts on performance. Topics include the virtual address space and
virtual memory, the heap and dynamic memory management, the memory
hierarchy and caching, assembly language and the stack, communication
and interrupts/signals, compiling and assemblers/ linkers. Enroll Info:
None
Requisites: E C E/COMP SCI 252 and (COMP SCI 300 or 302) or
graduate/professional standing or declared in the Capstone Certificate
in Computer Sciences for Professionals
Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth
- Natural Science
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 368 — LEARNING A PROGRAMMING LANGUAGE 1 credit.
For students interested in learning a particular programming language.
Focuses on a specific language offered at one of three levels:
beginner, intermediate, and advanced. Students may repeat the course
if the topic title is different. Enroll Info: None
Requisites: None
Course Designation: Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
COMP SCI 369 — WEB PROGRAMMING
3 credits.
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. Enroll Info: None
Requisites: (COMP SCI 300 or 367), graduate/professional standing,
or declared in the Capstone Certificate in Computer Sciences for
Professionals
Course Designation: Breadth - Natural Science
Level - Elementary
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2014
Computer Sciences (COMP SCI) 3
COMP SCI 400 — PROGRAMMING III
3 credits.
The third course in our programming fundamentals sequence. It presumes
that students understand and use functional and object oriented design
and abstract data types as needed. This course introduces balanced
search trees, graphs, graph traversal algorithms, hash tables and
sets, and complexity analysis and about classes of problems that
require each data type. Students are required to design and implement
using high quality professional code, a medium sized program, that
demonstrates knowledge and use of latest language features, tools, and
conventions. Additional topics introduced will include as needed for
projects: inheritance and polymorphism; anonymous inner classes,
lambda functions, performance analysis to discover and optimize
critical code blocks. Students learn about industry standards for code
development. Students will design and implement a medium size project
with a more advanced user-interface design, such as a web or mobile
application with a GUI and event- driven implementation; use of
version-control software. Enroll Info: None
Requisites: COMP SCI 300, graduate/professional standing, or
declared in the Capstone Certificate in Computer Sciences for
Professionals Course Designation: Breadth - Natural Science
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 402 — INTRODUCING COMPUTER SCIENCE TO K-12 STUDENTS
2 credits.
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. Enroll Info: None
Requisites: (COMP SCI 200, 202, 220, 300, 301, 302, 310, or 367),
graduate/professional standing, or declared in the Capstone
Certificate in Computer Sciences for Professionals
Course Designation: Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 407 — FOUNDATIONS OF MOBILE SYSTEMS AND APPLICATIONS
3 credits.
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. Enroll Info: None
Requisites: (COMP SCI 300 or 367), graduate/professional standing,
or declared in the Capstone Certificate in Computer Sciences for
Professionals
Course Designation: Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2020
4 Computer Sciences (COMP SCI)
COMP SCI 412 — INTRODUCTION TO NUMERICAL METHODS 3 credits.
Interpolation, solution of linear and nonlinear systems of equations,
approximate integration and differentiation, numerical solution of
ordinary differential equations, Data fitting (such as least squares)
by polynomials and splines. Knowledge of matrix algebra recommended,
such as MATH 340. Enroll Info: None
Requisites: MATH 222 and (MATH/COMP SCI 240 or MATH 234) and (COMP
SCI 200, 300, 301, 302, or 310) or graduate/professional standing or
declared in the Capstone Certificate in Computer Sciences for
Professionals
Course Designation: Breadth - Natural Science
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI/I SY E/MATH 425 — INTRODUCTION TO COMBINATORIAL
OPTIMIZATION
3 credits.
Focuses on optimization problems over discrete structures, such as
shortest paths, spanning trees, flows, matchings, and the traveling
salesman problem. We will investigate structural properties of these
problems, and we will study both exact methods for their solution, and
approximation algorithms. Enroll Info: None
Requisites: (MATH 320, 340, 341, or 375) or graduate/professional
standing or member of the Pre-Masters Mathematics (Visiting
International) Program
Course Designation: Breadth - Physical Sci. Counts toward the
Natural Sci req
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI/E C E/MATH 435 — INTRODUCTION TO CRYPTOGRAPHY 3
credits.
Cryptography is the art and science of transmitting digital
information in a secure manner. Provides an introduction to its
technical aspects. Enroll Info: None
Requisites: (MATH 320, 340, 341, or 375) or graduate/professional
standing or member of the Pre-Masters Mathematics (Visiting
International) Program
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/STAT 471 — INTRODUCTION TO COMPUTATIONAL STATISTICS
3 credits.
Classical statistical procedures arise where closed-form mathematical
expressions are available for various inference summaries (e.g. linear
regression; analysis of variance). A major emphasis of modern
statistics is the development of inference principles in cases where
both more complex data structures are involved and where more
elaborate computations are required. Topics from numerical linear
algebra, optimization, Monte Carlo (including Markov chain Monte
Carlo), and graph theory are developed, especially as they relate to
statistical inference (e.g., bootstrapping, permutation, Bayesian
inference, EM algorithm, multivariate analysis). Enroll Info: None
Requisites: (STAT/MATH 310 and STAT 333) or graduate/professional
standing
Course Designation: Breadth - Natural Science
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI/MATH/STAT 475 — INTRODUCTION TO COMBINATORICS 3
credits.
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. Problem solving. Enroll Info: None
Requisites: (MATH 320, 340, 341, or 375) or graduate/professional
standing or member of the Pre-Masters Mathematics (Visiting
International) Program
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/CURRIC 502 — THEORY AND PRACTICE IN COMPUTER SCIENCE
EDUCATION
1 credit.
Computer science educational pedagogy and general teaching practices.
Practical experience gained through tutoring students. Knowledge of
object-oriented programming required. Enroll Info: None
Requisites: COMP SCI 300 or 302 or declared in Computer Science
graduate program
Course Designation: Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/E C E 506 — SOFTWARE ENGINEERING
3 credits.
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. Enroll Info:
None
Requisites: (COMP SCI 367 or 400) and (COMP SCI 407, 536, 537, 545,
559, 564, 570, 679 or E C E/COMP SCI 552) or graduate/professional
standing, or declared in the Capstone Certificate in Computer Sciences
for Professionals
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/MATH 513 — NUMERICAL LINEAR ALGEBRA 3 credits.
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. Enroll Info: None
Requisites: (MATH 340, 341, or 375) and (COMP SCI 200, 300, 301,
302 or 310) or graduate/professional standing or member of the
Pre-Masters Mathematics (Visiting International) program
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
Last Taught: Spring 2021
COMP SCI/MATH 514 — NUMERICAL ANALYSIS
3 credits.
Polynomial forms, divided differences. Polynomial interpolation.
Polynomial approximation: uniform approximation and Chebyshev
polynomials, least-squares approximation and orthogonal polynomials.
Numerical differentiation and integration. Splines, B-splines and
spline approximation. Numerical methods for solving initial and
boundary value problems for ordinary differential equations. Enroll
Info: None Requisites: (MATH 320, 340, 341, or 375) and (MATH 322,
376, 421, or 521) and (COMP SCI 200, 220, 300, 310, or 301 prior to
Spring 2020) or graduate/professional standing or member of the
Pre-Masters Mathematics (Visiting International) Program
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
Last Taught: Fall 2020
Computer Sciences (COMP SCI) 5
COMP SCI/DS/I SY E 518 — WEARABLE TECHNOLOGY
3 credits.
Gives students hands-on experience in building wearable computing
platforms. Designed for students who have a background in textiles and
apparel design, computer science, engineering or media arts. By the
completion of the course students will have fundamental knowledge of
electronic circuitry, programming, and "maker skills". Enroll Info:
None Requisites: Sophomore standing
Course Designation: Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
Last Taught: Spring 2019
COMP SCI 520 — INTRODUCTION TO THEORY OF COMPUTING 3 credits.
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. Enroll Info: None
Requisites: (MATH/COMP SCI 240 or STAT/COMP SCI/MATH 475) and
(COMP SCI 367 or 400), or graduate/professional standing, or declared
in the Capstone Certificate in Computer Sciences for Professionals
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/E C E/I SY E 524 — INTRODUCTION TO OPTIMIZATION 3
credits.
Introduction to mathematical optimization from a modeling and solution
perspective. Formulation of applications as discrete and continuous
optimization problems and equilibrium models. Survey and appropriate
usage of basic algorithms, data and software tools, including modeling
languages and subroutine libraries. Enroll Info: None
Requisites: (COMP SCI 200, 220, 300, 301, 302, or 310) and (MATH
320, 340, 341, or 375) or graduate/professional standing
Course Designation: Breadth - Natural Science
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
6 Computer Sciences (COMP SCI)
COMP SCI/I SY E/MATH/STAT 525 — LINEAR OPTIMIZATION 3 credits.
Introduces optimization problems whose constraints are expressed by
linear inequalities. Develops geometric and algebraic insights into
the structure of the problem, with an emphasis on formal proofs.
Presents
the theory behind the simplex method, the main algorithm used to solve
linear optimization problems. Explores duality theory and theorems of
the alternatives. Enroll Info: None
Requisites: MATH 320, 340, 341, 375, or 443 or
graduate/professional standing or member of the Pre-Masters
Mathematics (Visiting International) Program
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
Last Taught: Spring 2021
COMP SCI/I SY E 526 — ADVANCED LINEAR PROGRAMMING 3 credits.
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. Enroll Info:
None Requisites: STAT/COMP SCI/I SY E/MATH 525 and (COMP SCI 200,
220, 300, 301, 302, or 310) or graduate/professional standing Course
Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2020
COMP SCI/E C E/M E 532 — MATRIX METHODS IN MACHINE LEARNING 3
credits.
Introduction to machine learning that focuses on matrix methods and
features real-world applications ranging from classification and
clustering to denoising and data analysis. Mathematical topics include:
linear equations, regression, regularization, the singular value
decomposition, and iterative algorithms. Machine learning topics
include: the lasso, support vector machines, kernel methods, clustering,
dictionary learning, neural networks, and deep learning. Previous
exposure to numerical computing (e.g. Matlab, Python, Julia, R)
required. Enroll Info: None Requisites: (MATH 222 or 276) and (E C E
203, COMP SCI 200, 220, 300, 301, 302, 310, or 320), or
graduate/professional standing, or declared in Capstone Certificate in
Computer Sciences for Professionals Course Designation: Breadth -
Physical Sci. Counts toward the Natural Sci req
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
Last Taught: Spring 2021
COMP SCI/E C E 533 — IMAGE PROCESSING
3 credits.
Mathematical representation of continuous and digital images; models
of image degradation; picture enhancement, restoration, segmentation,
and coding; pattern recognition, tomography. Enroll Info: None
Requisites: E C E 330 and (MATH 320 or 340), graduate/professional
standing, or member of Engineering Guest Students
Course Designation: Breadth - Physical Sci. Counts toward the
Natural Sci req
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
Last Taught: Fall 2020
COMP SCI 534 — COMPUTATIONAL PHOTOGRAPHY
3 credits.
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. Enroll Info: None
Requisites: (COMP SCI 300 or 367) and (MATH 217, 221 or 275) or
graduate/professional standing or declared in the Capstone Certificate
in Computer Sciences for Professionals
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Fall 2018
COMP SCI 536 — INTRODUCTION TO PROGRAMMING LANGUAGES AND
COMPILERS
3 credits.
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.
Enroll Info: None
Requisites: E C E/COMP SCI 354 and (COMP SCI 367 or 400) or
graduate/professional standing or declared in the Capstone Certificate
in Computer Sciences for Professionals
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 537 — INTRODUCTION TO OPERATING SYSTEMS 4 credits.
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. Enroll Info: None Requisites:
E C E/COMP SCI 354 and (COMP SCI 367 or 400) or graduate/professional
standing or declared in the Capstone Certificate in Computer Sciences
for Professionals
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 538 — INTRODUCTION TO THE THEORY AND DESIGN OF
PROGRAMMING LANGUAGES
3 credits.
Design and theory of programming languages: procedural, object
oriented, functional and logic paradigms. Serial and concurrent
programming. Execution models and formal specification techniques.
Enroll Info: None
Requisites: E C E/COMP SCI 354 and (COMP SCI 367 or 400) or
graduate/professional standing or declared in the Capstone Certificate
in Computer Sciences for Professionals
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2020
COMP SCI/E C E/M E 539 — INTRODUCTION TO ARTIFICIAL NEURAL
NETWORKS
3 credits.
Theory and applications of artificial neural networks: multi-layer
perceptron, self-organization mapdeep neural network convolutional
neural network, recurrent network, support vector machines genetic
algorithm, and evolution computing. Applications to control, pattern
recognition, prediction, and object detection and tracking. Enroll Info:
None
Requisites: COMP SCI 200, 220, 300, 301, 302, or 310 or graduate/
professional standing
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Fall 2020
Computer Sciences (COMP SCI) 7
COMP SCI 540 — INTRODUCTION TO ARTIFICIAL INTELLIGENCE 3
credits.
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. Enroll Info: None
Requisites: (COMP SCI 300, 320 or 367) and (MATH 211, 217, 221, or
275) or graduate/professional standing or declared in the Capstone
Certificate in Computer Sciences for Professionals
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 542 — INTRODUCTION TO SOFTWARE SECURITY 3 credits.
Teaches the security considerations that occur during all steps of the
software development life cycle: methodologies for designing secure
software, programming using secure programming techniques, in-depth
vulnerability assessment methodologies, static and dynamic analysis
tools for evaluating software security, and system defenses reducing
security threats. Enroll Info: None
Requisites: COMP SCI 400 or 320, graduate/professional standing,
or declared in the Capstone Certificate in Computer Sciences for
Professionals
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 545 — NATURAL LANGUAGE AND COMPUTING 3 credits.
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 interfacing, and machine translation. Enroll Info: None
Requisites: COMP SCI 536, 537, or 564 or graduate/professional
standing or declared in the Capstone Certificate in Computer Sciences
for Professionals
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
Last Taught: Fall 2014
8 Computer Sciences (COMP SCI)
COMP SCI/E C E 552 — INTRODUCTION TO COMPUTER ARCHITECTURE 3
credits.
The design of computer systems and components. Processor design,
instruction set design, and addressing; control structures and
microprogramming; memory management, caches, and memory hierarchies; and
interrupts and I/O structures. E C E 551 or knowledge of Verilog is
recommended. Enroll Info: None
Requisites: (E C E/COMP SCI 352 and E C E/COMP SCI 354) or
graduate/ professional standing
Course Designation: Breadth - Physical Sci. Counts toward the
Natural Sci req
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/I SY E/M E 558 — INTRODUCTION TO COMPUTATIONAL
GEOMETRY
3 credits.
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.
Enroll Info: None Requisites: (COMP SCI 367 or 400) and MATH 234
or graduate/ professional standing
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
Last Taught: Spring 2018
COMP SCI 559 — COMPUTER GRAPHICS
3 credits.
Survey of computer graphics. Image representation, formation,
presentation, composition and manipulation. Modeling, transformation,
and display of geometric objects in two and three dimensions.
Representation of curves and surfaces. Rendering, animation, multi
media and visualization. Fluency with vector mathematics (e.g., from
MATH 234 or a linear algebra class) is recommended. Enroll Info: None
Requisites: (MATH 222 or MATH 276) and (COMP SCI 367 or 400) or
graduate/professional standing or declared in the Capstone Certificate
in Computer Sciences for Professionals
Course Designation: Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/E C E 561 — PROBABILITY AND INFORMATION THEORY IN
MACHINE LEARNING
3 credits.
Probabilistic tools for machine learning and analysis of real-world
datasets. Mathematical topics include Bayesian probability and
statistics, random vectors, categorical random variables, entropy,
mutual information, and source coding. Machine Learning topics include
Bayes classification, Naive Bayes, generative modeling, decision trees
and random forests, maximum likelihood estimation, principal component
analysis, stochastic gradient methods, empirical risk minimization,
entropy minimization, dimensionality reduction with random
projections, and variational autoencoders. Previous exposure to
numerical computing (e.g. Matlab, Python, Julia, R) required. Enroll
Info: None Requisites: (MATH 320, 340, 341, 375, or M E/COMP SCI/E
C E 532 or concurrent enrollment) and (E C E 331, STAT/MATH 309, 431,
STAT 311, 324, M E/STAT 424 or MATH 531) or grad/profsnl standing or
declared in Capstone Certificate in Computer Sciences for
Professionals Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
COMP SCI 564 — DATABASE MANAGEMENT SYSTEMS: DESIGN AND
IMPLEMENTATION
4 credits.
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. Enroll Info: None
Requisites: E C E/COMP SCI 354 and (COMP SCI 367 or 400) or
graduate/professional standing or declared in the Capstone Certificate
in Computer Sciences for Professionals
Course Designation: Breadth - Physical Sci. Counts toward the
Natural Sci req
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/B M I 567 — MEDICAL IMAGE ANALYSIS
3 credits.
Present introductory medical image processing and analysis techniques.
Topics include medical imaging formats, segmentation, registration,
image quantification, classification. Strongly encourage Matlab
experience, such as COMP SCI 310 or 368-Matlab. Enroll Info: None
Requisites: (MATH 320 or 340) and (STAT 511, 541, POP HLTH/B M I
551, STAT 324, 371, or STAT/F&W ECOL/HORT 571) or
graduate/professional standing
Course Designation: Breadth - Biological Sci. Counts toward the
Natural Sci req
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2020
COMP SCI 570 — INTRODUCTION TO HUMAN-COMPUTER INTERACTION 4
credits.
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 month long
group projects. Meets with COMP SCI 270. Enroll Info: None
Requisites: (COMP SCI 200, 202, 300, 301, or 302) or graduate/
professional standing or declared in the Capstone Certificate in
Computer Sciences for Professionals. Not open to students who have
completed COMP SCI 270.
Course Designation: Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/B M I 576 — INTRODUCTION TO BIOINFORMATICS 3 credits.
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. Enroll Info: None
Requisites: (COMP SCI 300 or 367) and MATH 222 or graduate/
professional standing or declared in the Capstone Certificate in
Computer Sciences for Professionals
Course Designation: Breadth - Biological Sci. Counts toward the
Natural Sci req
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI 577 — INTRODUCTION TO ALGORITHMS
4 credits.
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. Enroll Info: None
Requisites: (MATH/COMP SCI 240 or STAT/COMP SCI/MATH 475) and
(COMP SCI 367 or 400), or graduate/professional standing, or declared
in the Capstone Certificate in Computer Sciences for Professionals
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
Computer Sciences (COMP SCI) 9
COMP SCI 578 — CONTEST-LEVEL PROGRAMMING
1 credit.
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. COMP SCI 577 is suggested but not required. Enroll
Info: None Requisites: (COMP SCI 300 or 367),
graduate/professional standing, or declared in the Capstone
Certificate in Computer Sciences for Professionals
Course Designation: Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Fall 2020
COMP SCI/DS 579 — VIRTUAL REALITY
3 credits.
Introduces students to the field of virtual reality and focuses on
creating immersive, interactive virtual experiences. Survey topics
include historical perspectives on virtual reality technology,
computer graphics and 3D modeling, human perception and psychology,
human computer interaction and user interface design. This course is
designed for students with backgrounds in Computer Science,
Engineering, Art, Architecture and Design. Students will work in
interdisciplinary teams on projects, culminating in a final event that
will be showcased to the public. While not an official uisite, the
class will be technologically motivated; therefore students should be
comfortable learning new software. The class will utilize publicly
available game design software which provides tools and services for
the creation of interactive content. While not necessary, students may
find it helpful to have taken classes in programming and computer
graphics (such COMP SCI 559: Computer Graphics) or in 3D modeling
(such as ART 429: 3D Digital Studio I or DS 242: Visual Communication
II). Enroll Info: None
Requisites: Sophomore standing
Course Designation: Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/L I S 611 — USER EXPERIENCE DESIGN 1
3 credits.
Introduction to the user experience design including key stages of the
design process, design ethics, and the methods and tools involved at
each stage of design. Conduct formative research on clients, users,
use contexts and tasks. Gain experience with user research
methodologies and learn to create intermediate design tools such as
personas. Develop and present a design proposal for a chosen project.
Enroll Info: None Requisites: Declared in Information MS, Design +
Innovation MS , or Capstone Certificate in User Experience Design
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
10 Computer Sciences (COMP SCI)
COMP SCI/L I S 612 — USER EXPERIENCE DESIGN 2
3 credits.
Advanced study of UX design. Introduces processes of ideation, key
concepts of visual design, conceptual and interaction design, low and
high-resolution prototyping of design techniques. Applications include
drafting designs based on user models and initial testing of prototypes.
Enroll Info: None
Requisites: COMP SCI/L I S 611 and Declared in Information MS,
Design + Innovation MS, or Capstone Certificate in User Experience
Design Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/L I S 613 — USER EXPERIENCE DESIGN 3
3 credits.
Conduct formal evaluations of the user experience (UX) or usability of
a digital system. Gain familiarity with the evaluation and research
process including key stages, tasks for each stage, common data
collection and analysis methods, and common tools employed in the
field. Gain experience with a variety of UX evaluation approaches.
Collect pilot data and develop a proposal for further UX testing.
Enroll Info: None Requisites: COMP SCI/L I S 612 and Declared in
Information MS, Design + Innovation MS, or Capstone Certificate in
User Experience Design Course Designation: Grad 50% - Counts
toward 50% graduate coursework requirement
Repeatable for Credit: No
Last Taught: Summer 2020
COMP SCI/L I S 614 — USER EXPERIENCE DESIGN CAPSTONE 1 credit.
Applies a design studio critique approach to produce a learning
environment of collaborative and interdisciplinary peer critique and
learning, in addition to provide expert feedback and suggestions.
Present and defend the latest iteration of the user experience design
project developed in earlier courses while learning about the
professions associated with digital user experience design. Enroll Info:
None Requisites: COMP SCI/L I S 613 and declared in Design +
Innovation, MS, or the Capstone Certificate in User Experience Design
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Summer 2020
COMP SCI/I SY E 635 — TOOLS AND ENVIRONMENTS FOR OPTIMIZATION
3 credits.
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. Enroll Info: None
Requisites: (MATH 320, 340, 341, or 375) and (COMP SCI 200, 202,
300, 301, or 302) or graduate/professional standing or declared in the
Capstone Certificate in Computer Sciences for Professionals Course
Designation: Breadth - Physical Sci. Counts toward the Natural Sci
req
Level - Intermediate
L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50%
- Counts toward 50% graduate coursework requirement Repeatable for
Credit: No
Last Taught: Spring 2015
COMP SCI 638 — UNDERGRADUATE TOPICS IN COMPUTING 1-4 credits.
Selected topics in computing. Each offering of the course will cover a
topic selected by the instructor and may cover one or more topics from
all of computer science. Enroll Info: None
Requisites: (COMP SCI 200, 202, 300, 301, 302, 310, or 367),
graduate/ professional standing, or declared in the Capstone
Certificate in Computer Sciences for Professionals
Course Designation: Breadth - Natural Science
Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
COMP SCI 639 — UNDERGRADUATE ELECTIVE TOPICS IN COMPUTING 3-4
credits.
Selected topics in computing. Each offering of the course will cover a
topic selected by the instructor. Offerings of this course will
provide sufficient depth into their subject to count as electives to
meet CS Major requirements. Enroll Info: None
Requisites: None
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
COMP SCI 640 — INTRODUCTION TO COMPUTER NETWORKS 3 credits.
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, TCP, and HTTP. Enroll Info: None
Requisites: COMP SCI 537 or graduate/professional standing or
declared in the Capstone Certificate in Computer Sciences for
Professionals Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 642 — INTRODUCTION TO INFORMATION SECURITY 3 credits.
Senior level undergraduate course covering various topics on
information security. Covers a wide range of topics, such as
cryptographic primitives, security protocols, system security, and
emerging topics. Elementary knowledge of mathematical logic and
discrete probability theory needed, such as MATH/COMP SCI 240. Enroll
Info: None
Requisites: COMP SCI 537 or graduate/professional standing or
declared in the Capstone Certificate in Computer Sciences for
Professionals Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 679 — COMPUTER GAME TECHNOLOGY
3 credits.
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. Enroll Info: None
Requisites: COMP SCI 559 or graduate/professional standing or
declared in the Capstone Certificate in Computer Sciences for
Professionals Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: No
Last Taught: Fall 2012
COMP SCI 681 — SENIOR HONORS THESIS
3 credits.
Individual study for seniors completing theses for honors in the
Computer Sciences major as arranged with a faculty member. Enroll
Info: None Requisites: Consent of instructor
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Honors -
Honors Only Courses (H)
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
COMP SCI 682 — SENIOR HONORS THESIS
3 credits.
Individual study for seniors completing theses for honors in the
Computer Sciences major as arranged with a faculty member.
Continuation of COMP SCI 681 Enroll Info: None
Requisites: Consent of instructor
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S Honors -
Honors Only Courses (H)
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
Computer Sciences (COMP SCI) 11
COMP SCI 691 — SENIOR THESIS
2-3 credits.
Individual study for seniors completing theses as arranged with a
faculty member. Enroll Info: None
Requisites: Consent of instructor
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Fall 2019
COMP SCI 692 — SENIOR THESIS
2-3 credits.
Individual study for seniors completing theses as arranged with a
faculty member, continuation of COMP SCI 691 Enroll Info: None
Requisites: Consent of instructor
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2020
COMP SCI 698 — DIRECTED STUDY
1-6 credits.
Directed study projects for juniors and seniors as arranged with a
faculty member. Enroll Info: None
Requisites: Consent of instructor
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2019
COMP SCI 699 — DIRECTED STUDY
1-6 credits.
Directed study projects for juniors and seniors as arranged with a
faculty member. Enroll Info: None
Requisites: Consent of instructor
Course Designation: Level - Advanced
L&S Credit - Counts as Liberal Arts and Science credit in L&S
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
COMP SCI 701 — CONSTRUCTION OF COMPILERS
3 credits.
Principles of the design and implementation of programming languages.
Topics include: Principles of compilation, static program analysis,
compilation methods to support profiling, and code-generation methods.
Knowledge of programming languages and compiler design strongly
encouraged, such as COMP SCI 536. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2020
12 Computer Sciences (COMP SCI)
COMP SCI 702 — GRADUATE COOPERATIVE EDUCATION 1-2 credits.
A full-time cooperative experience (10 or 5 week option) in computer
science in a commercial, government, or nonprofit setting, usually in
the form of an internship. The work will be monitored by a graduate
advisor from the faculty of the Department of Computer Sciences. Enroll
Info: 10 week option for 2 credits, 5 week option for 1 credit.
Requisites: Consent of instructor
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: Yes, for 2 number of completions Last
Taught: Spring 2021
COMP SCI 703 — PROGRAM VERIFICATION AND SYNTHESIS 3 credits.
Formal methods for program verification. Model-checking techniques;
linear temporal logic; computational tree logic; logic/automata
connection; bisimulations; probabilistic model-checking. Special
topics include: program synthesis, verification and synthesis of
privacy properties. Enroll Info: Knowledge of programming languages
and compiler design strongly encouraged, such as COMP SCI 536.
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI 704 — PRINCIPLES OF PROGRAMMING LANGUAGES 3 credits.
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. Students are strongly
encouraged to have knowledge of programming languages, such as from
COMP SCI 536. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 706 — ANALYSIS OF SOFTWARE ARTIFACTS 3 credits.
Advanced course covering various analysis techniques used in software
engineering. Covers 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. Students
are strongly encouraged to have knowledge of programming languages and
compiler design, such as COMP SCI 536, and a basic knowledge of
mathematical logic. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2017
COMP SCI/E C E 707 — MOBILE AND WIRELESS NETWORKING 3 credits.
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. Knower of computer networking is strongly
encouraged, such as from COMP SCI 640 or E C E 537. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI 710 — COMPUTATIONAL COMPLEXITY
3 credits.
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.
Knowledge of the theory of computation is strongly encouraged, such as
COMP SCI 520. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/MATH 714 — METHODS OF COMPUTATIONAL MATHEMATICS I
3 credits.
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. Enroll Info: Students are
strongly encouraged to have programming skills (e.g. COMP SCI 200) and
some undergraduate numerical analysis (e.g. MATH/COMP SCI 514 or COMP
SCI 412), analysis and differential equations (e.g. MATH 322 and MATH
521) and linear algebra (e.g. MATH 341).
Requisites: Graduate/professional standing or member of the Pre
Masters Mathematics (Visiting International) Program
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI/MATH 715 — METHODS OF COMPUTATIONAL MATHEMATICS II
3 credits.
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. Applications from science and
engineering. Enroll Info: Students are strongly encouraged to have
programming skills (e.g. COMP SCI 200), undergraduate numerical analysis
(e.g. MATH/COMP SCI 514 or COMP SCI 412), analysis (MATH 322 and math
521) and linear algebra (e.g. MATH 341 or equiv.) Requisites:
Graduate/professional standing or member of the Pre Masters Mathematics
(Visiting International) Program
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/I SY E 719 — STOCHASTIC PROGRAMMING 3 credits.
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.
Students are strongly encouraged to have knowledge of linear
programming (e.g., MATH/COMP SCI/I SY E/STAT 525) and probability and
statistics (e.g., MATH/STAT 431). Knowledge of integer optimization
(MATH/COMP SCI/ I SY E 728) is helpful, but not required. Enroll Info:
None Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2020
COMP SCI/I SY E 723 — DYNAMIC PROGRAMMING AND ASSOCIATED TOPICS
3 credits.
General and special techniques of dynamic programming developed by
means of examples. Shortest-path algorithms. Deterministic equipment
replacement models. Resource allocation problem. Traveling-salesman
problem. Knapsack problem. Analysis of inventory systems. General
stochastic formulations. Markovian decision processes. Students are
strongly encouraged to have knowledge of mathematical optimization
(e.g., COMP SCI/I SY E/MATH/STAT 525, I SY E 623, COMP SCI/I SY E/
MATH/STAT 726), knowledge of analysis (e.g., MATH/STAT 431 or 521) and
programming ability (e.g., COMP SCI 200 or 301) Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
Computer Sciences (COMP SCI) 13
COMP SCI/I SY E/MATH/STAT 726 — NONLINEAR OPTIMIZATION I 3
credits.
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. Enroll Info: Students are
strongly encouraged to have knowledge of linear algebra and
familiarity with basic mathematical analysis.
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/I SY E 727 — CONVEX ANALYSIS
3 credits.
Convex sets in finite-dimensional spaces: relative interiors,
separation, set operations. Convex functions: conjugacy,
subdifferentials and directional derivations, functional operations,
Fenchel-Rockafellar duality. Applications to operations research and
related areas. Students taking this course are strongly encouraged to
have had a course in basic analysis (e.g. MATH 521) and a course in
linear algebra (e.g., MATH 340). Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI/I SY E/MATH 728 — INTEGER OPTIMIZATION 3 credits.
Introduces optimization problems over integers, and surveys the theory
behind the algorithms used in state-of-the-art methods for solving
such problems. Special attention is given to the polyhedral
formulations of these problems, and to their algebraic and geometric
properties. Applicability of Integer Optimization is highlighted with
applications in combinatorial optimization. Key topics include:
formulations, relaxations, polyhedral theory, cutting planes,
decomposition, enumeration. Students are strongly encouraged to have
knowledge of Linear Programming (e.g., MATH/COMP SCI/I SY E/STAT 525),
including algorithms, duality and polyhedral theory. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
14 Computer Sciences (COMP SCI)
COMP SCI/I SY E/MATH 730 — NONLINEAR OPTIMIZATION II 3 credits.
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. Enroll Info: None
Requisites: STAT/COMP SCI/I SY E/MATH 726
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2020
COMP SCI 733 — COMPUTATIONAL METHODS FOR LARGE SPARSE SYSTEMS
3 credits.
Algorithms and theory for large scale systems in engineering and
science, with emphasis on sparse matrices and iterative methods.
Students are strongly encouraged to have knowledge of data structures
(e.g., COMP SCI 367 or COMP SCI 300), numerical analysis (e.g.,
COMP SCI 412, E C E 334), and linear and matrix algebra (e.g., MATH
340). Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2012
COMP SCI 736 — ADVANCED OPERATING SYSTEMS
3 credits.
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. Enroll Info:
COMP SCI 537 or cons inst
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI 739 — DISTRIBUTED SYSTEMS
3 credits.
Basic concepts; distributed programming; distributed file systems;
atomic actions; fault tolerance, transactions, program and 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. Enroll Info: None
Requisites: COMP SCI 736
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 740 — ADVANCED COMPUTER NETWORKS
3 credits.
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. Students are strongly encouraged to
have knowledge of computer network design and protocols (e.g., COMP
SCI 640) Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI 744 — BIG DATA SYSTEMS
3 credits.
Issues in the design and implementation of big data processing
systems, including: an overview of cluster architecture, key design
goals (flexibility, performance and fault tolerance), popular
execution frameworks, basic abstractions, and applications (e.g.,
batch analytics, stream processing, graph processing, and machine
learning). Enroll Info: None Requisites: Graduate/professional
standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI/E C E 750 — REAL-TIME COMPUTING SYSTEMS 3 credits.
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. Students
are strongly encouraged to have knowledge of computer architecture
(e.g., E C E/COMP SCI 552) and operating system functions (e.g., COMP
SCI 537) Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2016
COMP SCI/E C E 752 — ADVANCED COMPUTER ARCHITECTURE I 3
credits.
Processor design, computer arithmetic, pipelining, multi-operation
processors, vector processors, control units, precise interrupts, main
memory, cache memories, instruction set design, stack machines, busses
and I/O, protection and security. Students are strongly encouraged to
have knowledge of computer architecture (e.g., E C E/COMP SCI 552).
Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/E C E 755 — VLSI SYSTEMS DESIGN
3 credits.
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. E C E 555 or equivalent
experience is strongly recommended. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2018
COMP SCI/E C E 756 — COMPUTER-AIDED DESIGN FOR VLSI 3 credits.
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. Students
are strongly encourage to have programming skills and to have taken a
course in Digital System Fundamentals such as E C E/COMP SCI 352.
Enroll Info: None Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2019
COMP SCI/E C E 757 — ADVANCED COMPUTER ARCHITECTURE II 3
credits.
Parallel algorithms, principles of parallelism detection and
vectorizing compilers, interconnection networks, MIMD machines,
processor synchronization, data coherence, multis, dataflow machines,
special purpose processors. Students are strongly encouraged to have
knowledge of computer architecture (e.g., E C E/COMP SCI 552). Enroll
Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 758 — ADVANCED TOPICS IN COMPUTER ARCHITECTURE 3
credits.
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. Students are strongly encouraged to have knowledge of
computer architecture (e.g., E C E/COMP SCI 552). Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
Computer Sciences (COMP SCI) 15
COMP SCI/E C E/E M A/E P/M E 759 — HIGH PERFORMANCE COMPUTING FOR
APPLICATIONS IN ENGINEERING
3 credits.
An overview of hardware and software solutions that enable the use of
advanced computing in tackling computationally intensive Engineering
problems. Hands-on learning promoted through programming assignments
that leverage emerging hardware architectures and use parallel
computing programming languages. Students are strongly encourage to
have completed COMP SCI 367 or COMP SCI 400 or to have equivalent
experience. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 760 — MACHINE LEARNING
3 credits.
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. Students are strongly encouraged
to have knowledge of introductory artificial intelligence (e.g., COMP
SCI 540). Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/E C E 761 — MATHEMATICAL FOUNDATIONS OF MACHINE
LEARNING
3 credits.
Mathematical foundations of machine learning theory and algorithms.
Probabilistic, algebraic, and geometric models and representations of
data, mathematical analysis of state-of-the-art learning algorithms
and optimization methods, and applications of machine learning.
Students should have taken a course in statistics and a course in
linear algebra (e.g., STAT 302 and MATH 341). Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
16 Computer Sciences (COMP SCI)
COMP SCI 763 — SECURITY AND PRIVACY FOR DATA SCIENCE 3 credits.
Security and privacy concerns in data science. Three core subjects will
be considered: Differential privacy and algorithmic fairness;
Adversarial machine learning; and Applied cryptography, especially with
applications to machine learning. In addition, a selection of more
advanced topics will be covered. Possible examples include additional
notions of privacy, language-based security, robust optimization. Enroll
Info: A firm grasp of probability/statistics (STAT/MATH 431) is
recommended. Previous exposure to at least one of cryptography (COMP
SCI/E C E/MATH 435), security (COMP SCI 642), and modern machine
learning (COMP SCI/ E C E/M E 539 or 540) is also recommended.
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI 764 — TOPICS IN DATABASE MANAGEMENT SYSTEMS 3 credits.
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. Students are strongly encouraged to have knowledge of
database design (e.g., COMP SCI 564). Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 765 — DATA VISUALIZATION
3 credits.
Principles of the visual presentation of data. Survey of Information
Visualization, Scientific Visualization, and Visual Analytics. Design
and evaluation of visualizations and interactive exploration tools.
Introduction to relevant foundations in visual design, human perception,
and data analysis. Encodings, layout and interaction. Approaches to
large data sets. Visualization of complex data types such as scalar
fields, graphs, sets, texts, and multi-variate data. Use of 2D, 3D and
motion in data presentations. Implementation issues. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI 766 — COMPUTER VISION
3 credits.
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
objects; shape analysis; texture analysis; goal-directed and model
based systems; parallel algorithms and special-purpose architectures.
Students are strongly encouraged to have basic proficiency in calculus
and linear algebra, such as MATH 340, and basic programming such as
COMP SCI 300 or COMP SCI 367. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/B M I 767 — COMPUTATIONAL METHODS FOR MEDICAL IMAGE
ANALYSIS
3 credits.
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. Students are strongly encouraged to have programming skills
and basic proficiency in calculus and linear algebra, such as MATH
340. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2016
COMP SCI 769 — ADVANCED NATURAL LANGUAGE PROCESSING 3 credits.
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. Students are strongly
encouraged to have knowledge of introductory artificial intelligence
(e.g., COMP SCI 540). Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2013
COMP SCI/ED PSYCH/PSYCH 770 — HUMAN-COMPUTER INTERACTION 3
credits.
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 applications in ubiquitous, affective,
assistive, social, and embodied computing. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI/B M I 776 — ADVANCED BIOINFORMATICS
3 credits.
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 analysis, protein and RNA structure prediction, and whole-genome
analysis and comparisons. Enroll Info: None Requisites:
Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 777 — COMPUTER ANIMATION
3 credits.
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. Students are strongly encouraged to have knowledge of
computer graphics (e.g., COMP SCI 559) Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2013
Computer Sciences (COMP SCI) 17
COMP SCI 784 — FOUNDATIONS OF DATA MANAGEMENT 3 credits.
Foundational concepts in databases and data management. The first part
of the course discusses topics on query languages (conjunctive
queries, Datalog), their expressivity and complexity of evaluation.
The
second part studies advanced topics in modern data management,
including data streams, massive parallelism, provenance, uncertain
data management and privacy. There are no specific course
prerequisites. It is strongly encouraged that the students are
familiar with databases and relational algebra (COMP SCI 564 or
equivalent). Knowledge of algorithms, complexity theory and
probability will also be helpful. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2021
COMP SCI 787 — ADVANCED ALGORITHMS
3 credits.
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. Students are strongly encouraged to have
introductory knowledge of algorithms (e.g., COMP SCI 577) Enroll Info:
None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI 790 — MASTER'S THESIS
1-9 credits.
Enroll Info: Grad st; Master's candidates only
Requisites: Consent of instructor
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
COMP SCI 799 — MASTER'S RESEARCH
1-9 credits.
Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
18 Computer Sciences (COMP SCI)
COMP SCI 809 — MATHEMATICAL TECHNIQUES IN THE ANALYSIS OF
ALGORITHMS
3 credits.
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. Students are strongly
encouraged to have knowledge of algorithms (e.g., COMP SCI 577) or
applied math analysis (e.g., MATH 321) and theory of probability
(e.g., MATH/STAT 431). Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2018
COMP SCI 812 — ARITHMETIC ALGORITHMS
3 credits.
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. Students are strongly encourage
to have knowledge of basic abstract algebra (e.g., MATH 541), and
intermediate programming ability (e.g., COMP SCI 367 or COMP SCI 300).
Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Spring 2020
COMP SCI 838 — TOPICS IN COMPUTING
1-3 credits.
Advanced topics of special interest to students in various areas of
Computer Science. Each offering of the course will cover a topic
selected by the instructor. Credit varies by offering - check with the
department to determine how an offering counts toward degree
requirements. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Fall 2020
COMP SCI 839 — CORE TOPICS IN COMPUTING
3 credits.
Topics selected from advanced areas. Enroll Info: None Requisites:
Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
COMP SCI/B M I/PSYCH 841 — COMPUTATIONAL COGNITIVE SCIENCE 3
credits.
Studies the biological and computational basis of intelligence, by
combining methods from cognitive science, artificial intelligence,
machine learning, computational biology, and cognitive neuroscience.
Requires ability to program. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI/E C E/STAT 861 — THEORETICAL FOUNDATIONS OF MACHINE
LEARNING
3 credits.
Advanced mathematical theory and methods of machine learning.
Statistical learning theory, Vapnik-Chevronenkis Theory, model
selection, high-dimensional models, nonparametric methods,
probabilistic analysis, optimization, learning paradigms. Enroll Info:
None
Requisites: E C E/COMP SCI 761 or E C E 830
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: No
Last Taught: Fall 2020
COMP SCI 880 — TOPICS IN THEORETICAL COMPUTER SCIENCE 3 credits.
Advanced topics in algorithms, complexity, and cryptography. The exact
topic varies. Enroll Info: None
Requisites: Graduate/professional standing
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
COMP SCI 899 — PRE-DISSERTATOR RESEARCH
1-9 credits.
Independent research supervised by a faculty member for students who
have completed a master's degree but have not reached dissertator
status. Enroll Info: None
Requisites: Consent of instructor
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
COMP SCI 900 — ADVANCED SEMINAR IN COMPUTER SCIENCE 0-1 credits.
Seminar on recent research on various aspects of computer science.
Enroll Info: None
Requisites: Consent of instructor
Course Designation: Grad 50% - Counts toward 50% graduate
coursework requirement
Repeatable for Credit: Yes, unlimited number of completions Last
Taught: Spring 2021
Computer Sciences (COMP SCI) 19
COMP SCI/B M E/B M I/BIOCHEM/CBE/GENETICS 915 — COMPUTATION
AND INFORMATICS IN BIOLOGY AND MEDICINE
1 credit.
Participants and outside speakers will discuss current research in
computation and informatics in biology and medicine. This seminar is
required of all CIBM program trainees. Enroll Info: None
Requisites: Consent of instructor
Course Designation: Grad 50% - Counts toward 50% graduate
coursework
requirement
Repeatable for Credit: Yes, unlimited number of completions
Last Taught: Spring 2021
COMP SCI 990 — DISSERTATION
1-6 credits.
Advanced level mentored reading and research for students with
dissertator status. Enroll Info: None
Requisites: Consent of instructor
Course Designation: Grad 50% - Counts toward 50% graduate
coursework
requirement
Repeatable for Credit: Yes, unlimited number of completions
Last Taught: Spring 2021
COMP SCI 999 — DISSERTATOR RESEARCH
1-6 credits.
Advanced level mentored reading and research for dissertators. Enroll
Info: None
Requisites: Consent of instructor
Course Designation: Grad 50% - Counts toward 50% graduate
coursework
requirement
Repeatable for Credit: Yes, unlimited number of completions
Last Taught: Spring 2021
[CS-List] WISC COMPUTER SCIENCES (COMP SCI)(University of Wisconsin–Madison)-
