CS 361 - Prob & Stat for Computer Sci

Fall 2021

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Prob & Stat for Computer SciCS361ADA72361DIS01100 - 1150 M  1109 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciCS361ADB66306DIS01200 - 1250 M  1109 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciCS361ADC66307DIS01300 - 1350 M  1109 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciCS361ADD66303DIS01400 - 1450 M  1109 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciCS361ADE66304DIS01500 - 1550 M  1310 Digital Computer Laboratory Hongye Liu
Prob & Stat for Computer SciCS361ADF66305OD01600 - 1650 M    Hongye Liu
Prob & Stat for Computer SciCS361ADG76052OD00900 - 0950 M    
Prob & Stat for Computer SciCS361ADH76054OD00900 - 0950 T    
Prob & Stat for Computer SciCS361AL166298LEC31230 - 1345 W F  190 Engineering Sciences Building Hongye Liu
Prob & Stat for Computer SciCS361AL276142OLC31230 - 1345 W F    Hongye Liu
Prob & Stat for Computer SciSTAT361ADA72362DIS01100 - 1150 M  1109 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciSTAT361ADB66311DIS01200 - 1250 M  1109 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciSTAT361ADC66312DIS01300 - 1350 M  1109 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciSTAT361ADD66308DIS01400 - 1450 M  1109 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciSTAT361ADE66309DIS01500 - 1550 M  1310 Digital Computer Laboratory Hongye Liu
Prob & Stat for Computer SciSTAT361ADF66310OD01600 - 1650 M    Hongye Liu
Prob & Stat for Computer SciSTAT361ADG76053OD00900 - 0950 M    
Prob & Stat for Computer SciSTAT361ADH76055OD00900 - 0950 T    
Prob & Stat for Computer SciSTAT361AL166299LEC31230 - 1345 W F  190 Engineering Sciences Building Hongye Liu
Prob & Stat for Computer SciSTAT361AL276143OLC31230 - 1345 W F    Hongye Liu

Official Description

Introduction to probability theory and statistics with applications to computer science. Topics include: visualizing datasets, summarizing data, basic descriptive statistics, conditional probability, independence, Bayes theorem, random variables, joint and conditional distributions, expectation, variance and covariance, central limit theorem. Markov inequality, Chebyshev inequality, law of large numbers, Markov chains, simulation, the PageRank algorithm, populations and sampling, sample mean, standard error, maximum likelihood estimation, Bayes estimation, hypothesis testing, confidence intervals, linear regression, principal component analysis, classification, and decision trees. Course Information: Same as STAT 361. Credit is not given for both CS 361 and ECE 313. Prerequisite: MATH 220 or MATH 221; credit or concurrent registration in one of MATH 225, MATH 257, MATH 415, MATH 416 or ASRM 406. For majors only.

Course Director

Text(s)

Forsyth, D. A. "Probability and Statistics for Computer Science," Springer (2018)

Learning Goals

Visualize and summarize data and reason about outliers and relationships (1), (3)

Apply the principles of probability to analyze and simulate random events (1)

Use inference to fit statistical models to data and evaluate how good the fit is (1), (3)

Apply machine learning tools to dimensionality reduction, classification, clustering, regression and hidden Markov model problems (1), (2), (6)

Topic List

visualizing datasets, summarizing data, basic descriptive statistics, conditional probability, independence, Bayes theorem, random variables, joint and conditional distributions, expectation, variance and covariance, central limit theorem. Markov inequality, Chebyshev inequality, law of large numbers, Markov chains, simulation, the PageRank algorithm, populations and sampling, sample mean, standard error, maximum likelihood estimation, Bayes estimation, hypothesis testing, confidence intervals, linear regression, principal component analysis, classification, decision trees, clustering and Markov chains

Last updated

2/7/2019by David Varodayan