CS177: Applications of Probability in CS

Jump to Today

CS177: Applications of Probability in Computer Science

University of California, Irvine, Fall 2025

Prof. Erik Sudderth

Probability and statistics play a key role in real-world applications of computer science. Examples include the modeling of text and web data, speech recognition, robotics, network traffic and system reliability modeling, probabilistic analysis of algorithms and graphs, machine learning and data mining, cryptography, and more. In this course, students will expand their knowledge of probabilistic models and methods, and apply them to diverse computational problems. The mathematical topics we will study include conditioning and Bayes' rule, joint distributions of discrete and continuous random variables, independence and conditional independence, rare events, limit theorems, entropy and information theory, and discrete-time Markov processes.

Course Materials

  • Ed Discussion will be used for all course announcements, discussions, and questions.  All enrolled students should watch for important announcements and post questions (anonymously if you prefer) about course content.
  • Lecture Calendar including readings, slides, and how to access lecture recordings
  • Discussion Calendar including links to Jupyter notebooks with example code
  • Homework Assignments are distributed via Canvas, but submitted and graded via gradescope
  • Homework Policies and Resources including help with Python and the Collaboration and Academic Honesty Policy

Course Information

  • Textbook:  Introduction to Probability for Computing.  Mor Halchol-Balter, Cambridge University Press, 2024.
  • Lectures:  Tuesdays and Thursdays from 11:00-12:20pm, SSPA 1100.
  • Discussions:  Led by the TAs on Fridays in PSCB 120 and HH 262.
  • Instructor:  Prof. Erik Sudderth.
  • Teaching Assistants:  Jimin Heo and Saatvik Kher and Junchen Zhao.
  • Office Hours:  
    • Mondays 2:00-4:00pm in DBH 4241:  Jimin Heo.
    • Tuesdays 1:00-3:00pm in DBH 4059:  Saatvik Kher.
    • Wednesdays 3:00-4:00pm in DBH 4206:  Prof. Sudderth.
    • Thursdays 1:00-3:00pm in DBH 3081:  Junchen Zhao.

Course Prerequisites

An introductory course in probability and statistics (STATS 67). Courses in calculus (MATH 2B), linear algebra (MATH 3A or I&C SCI 6N), and discrete mathematics (I&C SCI 6B, I&C SCI 6D). Homework assignments require some Python programming experience, and knowledge about basic data structures and algorithms. 

Exams and Course Grades

Overall course grades will be assigned as follows: 40% homeworks, 30% for two midterm exams, 30% final exam.  To determine an overall homework score, we will drop your lowest homework score, and average the scores of the other five homeworks equally.   In cases of academic dishonesty, we may determine course grades in other ways.

The midterm exams will be taken in-person during the normal lecture times on October 28 and November 18.  The final exam will be taken in-person on Tuesday, December 9 from 10:30-12:30pm.  Exams must be taken on their scheduled dates. Exceptions are granted only for medical or family emergencies.

Exams will test concepts from lectures and homeworks, and also require some computations to analyze probabilistic models.  During exams, use of electronic devices is not allowed.  However, you are allowed to bring a single, 8.5x11-inch page (both sides) of handwritten notes.  We will also provide a reference page with useful mathematical formulas.

Course Summary:

Course Summary
Date Details Due
CC Attribution Non-Commercial Share Alike This course content is offered under a CC Attribution Non-Commercial Share Alike license. Content in this course can be considered under this license unless otherwise noted.