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Course Information

ECS 132: Probability and Statistical Modeling for Computer Science​

Course Description​

This course provides a comprehensive introduction to probability theory and statistical modeling with a focus on applications in computer science. Students will learn fundamental concepts including probability spaces, random variables, distributions, statistical estimation, and hypothesis testing.

Learning Objectives​

By the end of this course, students will be able to:

  1. Apply fundamental principles of probability theory to solve problems
  2. Work with discrete and continuous probability distributions
  3. Understand and apply the Law of Large Numbers and Central Limit Theorem
  4. Perform maximum likelihood estimation
  5. Conduct hypothesis testing and binary classification
  6. Apply linear regression techniques
  7. Work with joint distributions and correlation

Course Format​

  • Lectures: Monday, Wednesday, Friday
  • Office Hours: [To be announced]
  • Communication: Canvas and course website

Prerequisites​

  • Calculus (MAT 21A-D or equivalent)
  • Programming experience (ECS 32B or equivalent)
  • Linear algebra recommended but not required

Textbooks and Resources​

Recommended textbooks and online resources will be provided on the course website.