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CS 361 - Prob & Stat for Computer Sci

Spring 2020

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Prob & Stat for Computer SciCS361ADA65086DIS00900 - 0950 W  1103 Siebel Center for Comp Sci Anay Pattanaik
Prob & Stat for Computer SciCS361ADB65087DIS01000 - 1050 W  1103 Siebel Center for Comp Sci Anay Pattanaik
Prob & Stat for Computer SciCS361ADC65083DIS01100 - 1150 W  1103 Siebel Center for Comp Sci Tiffany Li
Prob & Stat for Computer SciCS361ADD65084DIS01200 - 1250 W  1103 Siebel Center for Comp Sci Tiffany Li
Prob & Stat for Computer SciCS361ADE65085DIS01300 - 1350 W  1103 Siebel Center for Comp Sci Ehsan Saleh
Prob & Stat for Computer SciCS361ADG70266DIS01500 - 1550 W  1103 Siebel Center for Comp Sci Jinglin Chen
Prob & Stat for Computer SciCS361ADH70268DIS01600 - 1650 W  1103 Siebel Center for Comp Sci Jinglin Chen
Prob & Stat for Computer SciCS361AL165082LEC31100 - 1215 T R  2079 Natural History Building Hongye Liu
Prob & Stat for Computer SciSTAT361ADA65114DIS00900 - 0950 W  1103 Siebel Center for Comp Sci Anay Pattanaik
Prob & Stat for Computer SciSTAT361ADB65115DIS01000 - 1050 W  1103 Siebel Center for Comp Sci Anay Pattanaik
Prob & Stat for Computer SciSTAT361ADC65111DIS01100 - 1150 W  1103 Siebel Center for Comp Sci Tiffany Li
Prob & Stat for Computer SciSTAT361ADD65112DIS01200 - 1250 W  1103 Siebel Center for Comp Sci Tiffany Li
Prob & Stat for Computer SciSTAT361ADE65113DIS01300 - 1350 W  1103 Siebel Center for Comp Sci Ehsan Saleh
Prob & Stat for Computer SciSTAT361ADG70267DIS01500 - 1550 W  1103 Siebel Center for Comp Sci Jinglin Chen
Prob & Stat for Computer SciSTAT361ADH70269DIS01600 - 1650 W  1103 Siebel Center for Comp Sci Jinglin Chen
Prob & Stat for Computer SciSTAT361AL165110LEC31100 - 1215 T R  2079 Natural History Building 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