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CS 473 - Algorithms

Spring 2020

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
AlgorithmsCS473AL131457LEC1530 - 1645 W F  1404 Siebel Center for Comp Sci Jeff G Erickson
AlgorithmsCS473AL231552LEC1530 - 1645 W F  1404 Siebel Center for Comp Sci Jeff G Erickson
AlgorithmsCSE414AL131560LEC1530 - 1645 W F  1404 Siebel Center for Comp Sci Jeff G Erickson
AlgorithmsCSE414AL258767LEC1530 - 1645 W F  1404 Siebel Center for Comp Sci Jeff G Erickson
AlgorithmsMATH473AL131577LEC1530 - 1645 W F  1404 Siebel Center for Comp Sci Jeff G Erickson
AlgorithmsMATH473AL258768LEC1530 - 1645 W F  1404 Siebel Center for Comp Sci Jeff G Erickson

Official Description

Design and analysis techniques, approximation algorithms, randomized algorithms and amortized analysis, and advanced topics such as network flow, linear programming, and dynamic data structures, among others. Course Information: Same as CSE 414 and MATH 473. 4 undergraduate hours. 4 graduate hours. Prerequisite: CS 374, and one of CS 361, MATH 461, or STAT 400.

Learning Goals

Know definitions of "core computational problems" and be able to describe various algorithms and their time and space complexities for such problems. (1,6)

Be able to abstract key computational elements and challenges from newly encountered problems in different domains, and identify when known algorithms or variants thereof may be employed to find a solution. (1,6)

Be able to design new algorithms using a variety of algorithmic approaches, including brute force, greedy, recursion, divide-and-conquer, dynamic programming, randomization, etc. (1,6)

Be able to use appropriate data structures such as arrays, linked lists, BSTs, heaps, hash tables, adjacency matrices or lists, etc, to solve problems efficiently. (1,6)

Be able to analyze the (worst case, randomized, amortized) time and space complexity of given algorithms using techniques such as loop summations, recurrences, charging arguments, potential functions, properties of probability. (1,6)

Be able to reason formally about problem difficulty using reductions, adversary arguments, decision trees, and similar techniques. (1,6)

More generally, be able to communicate and reason about computation clearly, formally, and concisely. (3,5)

Topic List

NP Completeness.
Dynamic Programming
Divide and Conquer and FFT
Randomized algorithms, analysis, and applications
Network flows, matchings and applications
Linear programming
Approximation algorithms
Additional topics vary

Assessment and Revisions

Revisions in last 6 years Approximately when revision was done Reason for revision Data or documentation available?
Reduced written homework, added online quizzes testing more basic material Spring 2011 The instructors recognized that many students were struggling with basic material, such as the behavior of algorithms presented in class, which is necessary to make progress on the homework. Automatically graded online quizzes were introduced as an experimental measure to attempt to address this gap. Promising initial results led us to make the addition permanent. Informal discussion
Removed oral homework grading Spring 2011 insufficient staffing to support increasing class sizes Informal discussion

Required, Elective, or Selected Elective

Selected Elective.

Last updated

3/9/2019