Probability and Statistics for Engineers
Instructor: Jeff Phillips (email) | Office hours: Thursday 9-10am @ MEB 3404 (and usually directly after class in ASB 220)
TAs: Yuan Zhuang (yuan.zhuang@utah.edu) | Help hours: Monday 10am-noon @ MEB 3115
         Meghna Manjunatha (u1368460@utah.edu) | Help hours: Monday 1:30-3:30pm @ MEB 3105
         Chris Harker (chris.harker@utah.edu) | Help hours: Monday 7-9pm (Zoom)
         Vishva Desai (u1368790@utah.edu) | Help hours: Tuesday 9-11am @ MEB 3515
         Ananya Smirti (u1419404@umail.utah.edu) | Help hours: Tuesday 11am-1pm @ MEB 3115
         Anna Bell (abell.datascience@gmail.com) | Help hours: Tuesday 1:25-3:25 @ MEB 3105
         Austin Li (u1364758@umail.utah.edu) | Help hours: Wednesday 3:30-4:30pm @ MEB 3115
         Meysam Alishahi (meysam.alishahi@gmail.com) | Help hours: Friday 10am-noon @ MEB 3515
Spring 2023 | Tuesdays, Thursdays 3:40 pm - 5:00 pm
ASB 220; Zoom; and YouTube
Catalog number: 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 due at the end of Friday. Students will be suggested to take them at the end of class, in class on Thursdays.

Schedule: (subject to slightly change)
Date Reading Topic Slides Video Assignment
Tu 1.10 Ch 1 Introduction - V
Th 1.12 Ch 2 Sample Spaces, Events S V
Quiz 0
Fr 1.13
Data Science Day
Union Ballroom
Tu 1.17 Ch 2 Basic Probability S V HW 1 out
Th 1.19 Ch 3 Conditional Probability S V
Quiz 1
Tu 1.24 Ch 3 Independence S V HW 1 due
Th 1.26 Ch 3 Total Probability S V
Quiz 2
Tu 1.31 Ch 3 Bayes Rule S V HW 2 out
Th 2.02 Ch 4 Discrete Random Variables S V
Quiz 3
Tu 2.07 Ch 4 Binomial and Geometric Distributions S - HW 2 due
Th 2.09 Ch 5 R for Discrete Distributions S -
Quiz 4
Tu 2.14 Ch 5 Continuous Random Variables S - HW 3 out
Th 2.16 Ch 5 Gaussian Random Variables S -
Quiz 5
Tu 2.21 Ch 7 Expectation S - HW 3 due
Th 2.23 Ch 7 Variance S -
Quiz 6
Tu 2.28 Ch 9 Joint Probability for Discrete RVs S - HW 4 out
Th 3.02 Ch 9 Joint Probability for Discrete and Continuous RVs S -
Quiz 7
Tu 3.07
SPRING BREAK
Th 3.09
SPRING BREAK
Tu 3.14 Ch 9 Joint Probability for Continuous RVs S - HW 4 due
Th 3.16 Ch 10 Covariance and Correlation S -
Quiz 8
Tu 3.21 Ch 10 Bivariate Gaussian + Gaussian and Bivariate in R S - HW 5 out
Th 3.23 Ch 15-17 Intro to Statistics + R examples S -
Quiz 9
Tu 3.28 Ch 19 Estimation and Bias S - HW 5 due
Th 3.30 Ch 23 Confidence Intervals S -
Quiz 10
Tu 4.04 Ch 23 Confidence Intervals S - HW 6 out
Th 4.06 Ch 6 more on Confidence Intervals S -
Quiz 11
Tu 4.11 Ch 25-26 Hypothesis Testing S - HW 6 due
Th 4.13 Ch 25-27 Hypothesis Testing (+ R code + pic) S -
Quiz 12
Tu 4.18 Ch 28 Covariance Hypothesis Testing (+ R code) S - HW 7 out
Th 4.20 Review S -
Quiz 13
Tu 4.25 Ch 17.4 + Ch 22 Linear Regression (+ R code + data) S - HW 7 due
Mo 5.01 3:30 - 5:30 FINAL EXAM (practice exam + solutions)