Course Websites

CS 361 - Prob & Stat for Computer Sci

Last offered Spring 2022

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.

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

TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Prob & Stat for Computer SciADA65086DIS00900 - 0950 W  1111 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciADB65087DIS01000 - 1050 W  1111 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciADC65083DIS01100 - 1150 W  1111 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciADD65084DIS01200 - 1250 W  1111 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciADE65085DIS01300 - 1350 W  1111 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciADF68207DIS01400 - 1450 W  1111 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciADG70266DIS01500 - 1550 W  1111 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciADH70268DIS01600 - 1650 W  1111 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciADI71011DIS01700 - 1750 W  1111 Siebel Center for Comp Sci Hongye Liu
Prob & Stat for Computer SciAL165082LEC31230 - 1345 T R  3039 Campus Instructional Facility Hongye Liu