CS 361

CS 361 - Prob & Stat for Computer Sci

Fall 2021

Title	Rubric	Section	CRN	Type	Hours	Times	Days	Location	Instructor
Prob & Stat for Computer Sci	CS361	ADA	72361	DIS	0	1100 - 1150	M	1109 Siebel Center for Comp Sci	Hongye Liu Ajay Fewell
Prob & Stat for Computer Sci	CS361	ADB	66306	DIS	0	1200 - 1250	M	1109 Siebel Center for Comp Sci	Hongye Liu Ajay Fewell
Prob & Stat for Computer Sci	CS361	ADC	66307	DIS	0	1300 - 1350	M	1109 Siebel Center for Comp Sci	Hongye Liu Weikai Xu
Prob & Stat for Computer Sci	CS361	ADD	66303	DIS	0	1400 - 1450	M	1109 Siebel Center for Comp Sci	Hongye Liu Weikai Xu
Prob & Stat for Computer Sci	CS361	ADE	66304	DIS	0	1500 - 1550	M	1310 Digital Computer Laboratory	Hongye Liu Aditya Karan
Prob & Stat for Computer Sci	CS361	ADF	66305	OD	0	1600 - 1650	M		Hongye Liu Zewei Long
Prob & Stat for Computer Sci	CS361	ADG	76052	OD	0	0900 - 0950	M		Nathan Deng
Prob & Stat for Computer Sci	CS361	ADH	76054	OD	0	0900 - 0950	T		Nathan Deng
Prob & Stat for Computer Sci	CS361	AL1	66298	LEC	3	1230 - 1345	W F	190 Engineering Sciences Building	Hongye Liu
Prob & Stat for Computer Sci	CS361	AL2	76142	OLC	3	1230 - 1345	W F		Hongye Liu
Prob & Stat for Computer Sci	STAT361	ADA	72362	DIS	0	1100 - 1150	M	1109 Siebel Center for Comp Sci	Hongye Liu Ajay Fewell
Prob & Stat for Computer Sci	STAT361	ADB	66311	DIS	0	1200 - 1250	M	1109 Siebel Center for Comp Sci	Hongye Liu Ajay Fewell
Prob & Stat for Computer Sci	STAT361	ADC	66312	DIS	0	1300 - 1350	M	1109 Siebel Center for Comp Sci	Hongye Liu Weikai Xu
Prob & Stat for Computer Sci	STAT361	ADD	66308	DIS	0	1400 - 1450	M	1109 Siebel Center for Comp Sci	Hongye Liu Weikai Xu
Prob & Stat for Computer Sci	STAT361	ADE	66309	DIS	0	1500 - 1550	M	1310 Digital Computer Laboratory	Hongye Liu Aditya Karan
Prob & Stat for Computer Sci	STAT361	ADF	66310	OD	0	1600 - 1650	M		Hongye Liu Zewei Long
Prob & Stat for Computer Sci	STAT361	ADG	76053	OD	0	0900 - 0950	M		Nathan Deng
Prob & Stat for Computer Sci	STAT361	ADH	76055	OD	0	0900 - 0950	T		Nathan Deng
Prob & Stat for Computer Sci	STAT361	AL1	66299	LEC	3	1230 - 1345	W F	190 Engineering Sciences Building	Hongye Liu
Prob & Stat for Computer Sci	STAT361	AL2	76143	OLC	3	1230 - 1345	W F		Hongye Liu

See full schedule from Course Explorer

Web Page

https://courses.grainger.illinois.edu/cs361/fa2021/index.html

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

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