CS 361

CS 361 - Prob & Stat for Computer Sci

Fall 2025

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Prob & Stat for Computer SciCS361ADB66306DIS01000 - 1050 M  0220 Siebel Center for Comp Sci Aditya Karan
Prob & Stat for Computer SciCS361ADC66307DIS01100 - 1150 M  0218 Siebel Center for Comp Sci Aditya Karan
Prob & Stat for Computer SciCS361ADD66303DIS01200 - 1250 M  0218 Siebel Center for Comp Sci Simon Kato
Prob & Stat for Computer SciCS361ADE66304DIS01300 - 1350 M  0218 Siebel Center for Comp Sci Ikhyun Cho
Prob & Stat for Computer SciCS361ADF66305DIS01400 - 1450 M  0218 Siebel Center for Comp Sci Ikhyun Cho
Prob & Stat for Computer SciCS361ADH76054DIS01600 - 1650 M  0218 Siebel Center for Comp Sci Simon Kato
Prob & Stat for Computer SciCS361AL166298LEC30930 - 1045 T R  0027/1025 Campus Instructional Facility Hongye Liu
Prob & Stat for Computer SciCS361CSP76056PKG31000 - 1050 M    Hongye Liu
Prob & Stat for Computer SciCS361CSP76056PKG3 -  ARR Illini Center Hongye Liu
Prob & Stat for Computer SciCS361CSP76056PKG3 -    Hongye Liu
Prob & Stat for Computer SciSTAT361ADB66311DIS01000 - 1050 M  0220 Siebel Center for Comp Sci Aditya Karan
Prob & Stat for Computer SciSTAT361ADC66312DIS01100 - 1150 M  0218 Siebel Center for Comp Sci Aditya Karan
Prob & Stat for Computer SciSTAT361ADD66308DIS01200 - 1250 M  0218 Siebel Center for Comp Sci Simon Kato
Prob & Stat for Computer SciSTAT361ADE66309DIS01300 - 1350 M  0218 Siebel Center for Comp Sci Ikhyun Cho
Prob & Stat for Computer SciSTAT361ADF66310DIS01400 - 1450 M  0218 Siebel Center for Comp Sci Ikhyun Cho
Prob & Stat for Computer SciSTAT361ADH76055DIS01600 - 1650 M  0218 Siebel Center for Comp Sci Simon Kato
Prob & Stat for Computer SciSTAT361AL166299LEC30930 - 1045 T R  0027/1025 Campus Instructional Facility Hongye Liu
Prob & Stat for Computer SciSTAT361CSP76057PKG31000 - 1050 M    Hongye Liu
Prob & Stat for Computer SciSTAT361CSP76057PKG3 -    Hongye Liu
Prob & Stat for Computer SciSTAT361CSP76057PKG3 -  ARR Illini Center 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