CS 361

CS 361 - Prob & Stat for Computer Sci

Fall 2026

Title	Rubric	Section	CRN	Type	Hours	Times	Days	Location	Instructor
Prob & Stat for Computer Sci	CS361	ADB	66306	DIS	0	1000 - 1050	M	0218 Siebel Center for Comp Sci	Aditya Karan
Prob & Stat for Computer Sci	CS361	ADC	66307	DIS	0	1100 - 1150	M	0218 Siebel Center for Comp Sci	Aditya Karan
Prob & Stat for Computer Sci	CS361	ADD	66303	DIS	0	1200 - 1250	M	0218 Siebel Center for Comp Sci	Simon Kato
Prob & Stat for Computer Sci	CS361	ADE	66304	DIS	0	1300 - 1350	M	0218 Siebel Center for Comp Sci	Ikhyun Cho
Prob & Stat for Computer Sci	CS361	ADF	66305	DIS	0	1400 - 1450	M	0218 Siebel Center for Comp Sci	Ikhyun Cho
Prob & Stat for Computer Sci	CS361	AL1	66298	LEC	3	1230 - 1345	T R	100 Materials Science & Eng Bld	Hongye Liu
Prob & Stat for Computer Sci	CS361	CSP	76056	PKG	3	-		ARR Illini Center	Hongye Liu
Prob & Stat for Computer Sci	CS361	CSP	76056	PKG	3	-			Hongye Liu
Prob & Stat for Computer Sci	CS361	CSP	76056	PKG	3	1000 - 1050	M		Hongye Liu

See full schedule from Course Explorer

Web Page

https://canvas.illinois.edu/courses/65254

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: Credit is not given toward graduation for: 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 227, MATH 257, MATH 415, MATH 416, ASRM 406 or BIOE 210.

Course Director

Hongye Liu

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