Announcements

See Course Introduction to learn about probabilistic systems and the course
See Some Math for review of some of the math facts and tools we will encounter in the course. Also see the mini-lectures on Sets and sums and Counting under Some math folder in Panopto Course Videos

Course Description

Introduction to probability and its role in modeling and analyzing real world phenomena and systems, including topics in statistics, machine learning, and statistical signal processing. Elements of probability, conditional probability, Bayes rule, independence. Discrete and continuous random variables. Signal detection. Functions of random variables. Expectation; mean, variance and covariance, linear MSE estimation. Conditional expectation; iterated expectation, MSE estimation, quantization and clustering. Parameter estimation. Classification. Sample averages. Inequalities and limit theorems. Confidence intervals. Prerequisites: Calculus at the level of MATH 51, CME 100 or equivalent and basic knowledge of computing at the level of CS106A.

Course Staff

Instructor

Professor Abbas El Gamal
Email: abbas@ee.stanford.edu
Office: 262 Packard Electrical Engineering Building
Office hours: Tuesday 3:00-4:00pm (starting 4/5) at 242 Packard
Wednesday 3:15-4:15pm (starting 3/30) at 262 Packard

Course Assistant

Kabir Chandrasekher
Email: kabirc@stanford.edu
Office: 239 Packard Electrical Engineering Building
Office hours: Wednesday 4:30-5:30pm (starting 4/6)

Administrative Assistant

Marsha Dillon
Email: marshad@stanford.edu

Lectures

Time: Tuesday, Thursday 11:30 AM - 1:00 PM
Place: HEWLETT103
Please check Syllabus for topics covered and a rough schedule
Attending all lectures is expected. To help you in case you miss a lecture, however, I will be uploading my Spring 2021 lecture videos organized by topic and lecture number here. These lectures will be somewhat different from the live lectures because I've made some improvements/changes to the lecture slides since then.

Review session

Time: Monday 5:15-6:15pm (starting 4/4)
Place: 214 Packard Electrical Engineering Building

Online collaboration

We will use Ed Discussion. You are automatically enrolled when you enroll in the class.

Reading

My slides will be the main reading material for this course. I will post the slide sets (for one or more lectures) under lecture slides. The slide sets will be posted before their corresponding lectures.

Credit

4-units for undergraduate students; 3-units for graduate students. Letter grade or CR/NC. This course satisfied an EE major requirement and UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

Course requirements and grading

Weekly homework sets: 60%

Nine homework sets each is assigned on Thursday starting 3/31. Each homework is due the following Wednesday before midnight. The homework sets will comprise a mixture of theoretical and computational problems using Python aimed at reinforcing concepts and demonstrating applications (please direct your questions on the Python code to Kabir). You can collaborate with other students, but you must submit your own homework. We will use Gradescope for posting, submitting and grading. The homeworks and solutions are posted here.

Weekly quizzes: 20%

Five quizzes, each assigned on Thursday at 7pm starting 4/14 and ending 5/12, and due before 7pm the following Saturday. You will be allowed 2 hours to work on each quiz (within the 48 hour window), including the time to scan and upload your solution. The quizzes are open class material only and are covered by the Stanford Honor Code. We will use Gradescope for quizzes. We will drop your lowest quiz score.

Final exam: 20%

Released 7pm on Friday 6/3 and due before 7pm on Sunday 6/5. So you will have 48 hours to do it. The exam is also open class material only and is covered by Stanford Honor Code. We will also use Gradescope.