CS 361

CS 361 - Prob & Stat for Computer Sci

Spring 2027

Title	Rubric	Section	CRN	Type	Hours	Times	Days	Location	Instructor
Prob & Stat for Computer Sci	CS361	ADA	65086	DIS	0	0900 - 0950	M	1304 Siebel Center for Comp Sci
Prob & Stat for Computer Sci	CS361	ADB	65087	DIS	0	1000 - 1050	M	1304 Siebel Center for Comp Sci
Prob & Stat for Computer Sci	CS361	ADC	65083	DIS	0	1100 - 1150	M	1304 Siebel Center for Comp Sci
Prob & Stat for Computer Sci	CS361	ADD	65084	DIS	0	1200 - 1250	M	1304 Siebel Center for Comp Sci
Prob & Stat for Computer Sci	CS361	ADE	65085	DIS	0	1300 - 1350	M	1304 Siebel Center for Comp Sci
Prob & Stat for Computer Sci	CS361	ADF	68207	DIS	0	1400 - 1450	M	1304 Siebel Center for Comp Sci
Prob & Stat for Computer Sci	CS361	ADG	70266	DIS	0	1500 - 1550	M	1304 Siebel Center for Comp Sci
Prob & Stat for Computer Sci	CS361	AL1	65082	LEC	3	1100 - 1215	T R	3039 Campus Instructional Facility	Hongye Liu

See full schedule from Course Explorer

Web Page

https://canvas.illinois.edu/courses/71603

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