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CS 361 - Prob & Stat for Computer Sci

Spring 2021

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Prob & Stat for Computer SciCS361ADA65086OD00900 - 0950 W    Weikai Xu
Prob & Stat for Computer SciCS361ADB65087OD01001 - 1050 W    Weikai Xu
Prob & Stat for Computer SciCS361ADC65083OD01100 - 1150 W    Sneha R Krishna Kumaran
Prob & Stat for Computer SciCS361ADD65084OD01200 - 1250 W    Sneha R Krishna Kumaran
Prob & Stat for Computer SciCS361ADE65085OD01300 - 1350 W    Aditya Karan
Prob & Stat for Computer SciCS361ADF68207OD01400 - 1450 W    Aditya Karan
Prob & Stat for Computer SciCS361ADG70266OD01500 - 1550 W    Yiren Wang
Prob & Stat for Computer SciCS361ADH70268OD01600 - 1650 W    Yiren Wang
Prob & Stat for Computer SciCS361AL165082OLC31100 - 1215 T R    Hongye Liu
Prob & Stat for Computer SciSTAT361ADA65114OD00900 - 0950 W    Weikai Xu
Prob & Stat for Computer SciSTAT361ADB65115OD01001 - 1050 W    Weikai Xu
Prob & Stat for Computer SciSTAT361ADC65111OD01100 - 1150 W    Sneha R Krishna Kumaran
Prob & Stat for Computer SciSTAT361ADD65112OD01200 - 1250 W    Sneha R Krishna Kumaran
Prob & Stat for Computer SciSTAT361ADE65113OD01300 - 1350 W    Aditya Karan
Prob & Stat for Computer SciSTAT361ADF70265OD01400 - 1450 W    Aditya Karan
Prob & Stat for Computer SciSTAT361ADG70267OD01500 - 1550 W    Yiren Wang
Prob & Stat for Computer SciSTAT361ADH70269OD01600 - 1650 W    Yiren Wang
Prob & Stat for Computer SciSTAT361AL165110OLC31100 - 1215 T R    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 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