Probability and Statistics for Engineers
Instructor: Ross Whitaker (email) | Office hours: Tues/Thurs 1pm-2pm @ WEB 3893
TAs:
Name Email Office Hours
Vishva Desai u1368790@utah.edu Monday, 9:35am - 11:35am, Rm 3115 MEB
Elham Ghelichkhan u1368673@utah.edu Thursday, 11am-1pm, Rm 3115 MEB
Fnu Harshit u1471783@utah.edu Friday, 1pm-3pm, Rm 3115 MEB
Ryan Lam u0674827@utah.edu Tuesday, 11am-1pm, Rm 3115 MEB
Freda Shi u1381017@utah.edu Monday, 3-5pm, Rm 3115 MEB
Ananya Smirti u1419404@utah.edu Wednesday, 3-5, Rm 3115 MEB


Class times: Tuesdays, Thursdays 3:40 pm - 5:00 pm
Location: ASB 220
Catalog numbers: CS 3130-01 and ECE 3530-01

Syllabus
Description:
An introduction to probability theory and statistics, with an emphasis on solving problems in computer science and engineering. Probability and statistics is an important foundation for computer science fields such as machine learning, artificial intelligence, computer graphics, randomized algorithms, image processing, and scientific simulations. Topics in probability include discrete and continuous random variables, probability distributions, sums and functions of random variables, the law of large numbers, and the central limit theorem. Topics in statistics include sample mean and variance, estimating distributions, correlation, regression, and hypothesis testing. Beyond the fundamentals, this course will also focus on modern computational methods such as simulation and the bootstrap. Students will learn statistical computing using the freely available R statistics software.

Book: A Modern Introduction to Probability and Statistics: Understanding Why and How by Dekking, Kraaikamp, Lopuhaa, and Meester.
An electronic version of this book is freely available through the University: here. To access the book you must be visiting this website from the campus network. Or if you are off campus, you can access it using VPN.

Homeworks will be due every 2 weeks on Tuesdays via GradeScope.

Quizzes take place each week, via Canvas. They will be released at the end of class on Thursday, and will be available until 5:15 on Thursday. Students are required to take them at the end of class, in class on Thursdays.

Schedule: (subject to slightly change) Notes,
Date Reading Topic Slides/Notes Video Assignment
Tu 1.09 Ch 1 Introduction Notes 1/9/24, Old V
Th 1.11 Ch 2 Sample Spaces, Events Notes 1/11/24, Old V
Quiz 0
Tu 1.16 Ch 2 Basic Probability, Conditional Probability Notes 1/16/24 Old V, Old V2 HW 1 out
Th 1.18 Ch 3 Independence Notes Old N 1/18/24, Old V
Quiz 1
Tu 1.23 Ch 3 Independence, Total Probability, Bayes Rule Notes, Old N 1/23/24 Old V HW 1 due, Solutions
Th 1.25 Ch 3 Bayes Rule, Random Variables Notes Old N N/A, Old V
Quiz 2
Tu 1.30 Ch 4 Discrete RVs Notes, Old N 1/30/24, Old V HW 2 out
Th 2.01 Ch 4, 5 Discrete Random Variables, Continuous RVs Notes Old N 2/1/24 (no sound), Old V
Quiz 3
Tu 2.06 Ch 5 Continuous RVs Old N 2/6/24 Old V HW 2 due, HW 3 out
Th 2.08 Ch 5, 7 R Intro (and Rmd) Notes, N/A, Old V
Quiz 4
Tu 2.13 Ch 5,7 Expectation/Variance Notes, Old N N/A, Old V
Th 2.15 Ch 7 R-docs, Expectation/Variance Notes, Old N 2/15/24, Old Video
Quiz 5
Tu 2.20 Ch 9 Joint Probability for Discrete RVs Notes Old N 2/20/24 , Old V HW 3 due
Th 2.22 Ch 9 Joint Probability for Discrete RVs Old N Old V
Quiz 6
Tu 2.27 Ch 9 Independence for RVs Old N Old V
Th 2.29 Midterm Exam Practice Midterm
Tu 3.05
SPRING BREAK
Th 3.07
SPRING BREAK
Tu 3.12 Ch 9 Independence for RVs Old N Old V
Quiz 7
Th 3.14 Ch 9 Joint Probability for Continuous RVs Old N Old V HW 4 due
Tu 3.19 Ch 10 Covariance and Correlation in R Old N Old V
Quiz 8
Th 3.21 Ch 15-17 Intro to Statistics + R examples Old N Old V
Tu 3.26 Ch 19 Estimation and Bias Old N Old V
Quiz 9
Th 3.28 Ch 23 Confidence Intervals Old N Old V HW 5 due
Tu 4.02 Ch 23 Confidence Intervals Old N Old V
Quiz 10
Th 4.04 Ch 23 Confidence Intervals Old N Old V
Tu 4.09 Ch 25-26 Hypothesis Testing Old N Old V
Quiz 11
Th 4.11 Ch 25-27 Hypothesis Testing Old N Old V HW 6 due
Tu 4.16 Ch 28 Hypothesis Testing (+ pic) Old N Old V
Quiz 12
Th 4.18 Ch 28 Hypothesis Testing in R + pic Old N Old V HW 7 out
Tu 4.23 Ch 17.4 + Ch 22 Linear Regression in R (+ data) Old N Old V
Quiz 13
Tu 4.30 3:30 - 5:30 FINAL EXAM (practice exam + soln1 | practice2 + soln2)