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CS 361 - Prob & Stat for Computer Sci

Fall 2020

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Prob & Stat for Computer SciCS361ADA72361OD01100 - 1150 M    
Prob & Stat for Computer SciCS361ADB66306OD01200 - 1250 M    
Prob & Stat for Computer SciCS361ADC66307OD01300 - 1350 M    
Prob & Stat for Computer SciCS361ADD66303OD01400 - 1450 M    
Prob & Stat for Computer SciCS361ADE66304OD01500 - 1550 M    
Prob & Stat for Computer SciCS361ADF66305OD01600 - 1650 M    
Prob & Stat for Computer SciCS361AL166298OLC31230 - 1345 T R    Hongye Liu
Prob & Stat for Computer SciSTAT361ADA72362OD01100 - 1150 M    
Prob & Stat for Computer SciSTAT361ADB66311OD01200 - 1250 M    
Prob & Stat for Computer SciSTAT361ADC66312OD01300 - 1350 M    
Prob & Stat for Computer SciSTAT361ADD66308OD01400 - 1450 M    
Prob & Stat for Computer SciSTAT361ADE66309OD01500 - 1550 M    
Prob & Stat for Computer SciSTAT361ADF66310OD01600 - 1650 M    
Prob & Stat for Computer SciSTAT361AL166299OLC31230 - 1345 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 or MATH 416. 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