1. Introduction to AI
  2. Jupyter Notebook Export Tutorial
  3. Week 1 - Introduction, Agents and Environments
  4. Week 2 - Probability and Bayesian Networks
  5. Week 3 - Advanced Probability, Bayesian Networks and D-Seperation
  6. Week 4 - Bayesian Networks and Review
  7. Week 5 – HMMs, Maximum Likelihood Estimation, EM Algorithm
  8. Week 6 – Coding Viterbi's Algorithm, HMMs, Maximum Likelihood Estimation, EM Algorithm and Review
  9. Week 7 – More Coding (Forward & Backward and Viterbi's)
  10. Week 8 - Likelihood Weighting, Expectation Maximation and MonteCarlo Methods
  11. Week 9 - Coding Practice and Intro to Reinforcement Learning
  12. Week 10 - Reinforcement Learning

UCSD CSE150A Winter 2025

Week 2 – Probability and Bayesian Networks

Homework 1 - Due 11:59 PM Sunday, January 26

Week 2 Lecture Materials

  • Week 2 Lecture Slides
    • Bayes' Theorem
    • Adv Topics in Prob., Part 1
  • Week 2 Notebooks

Week 2 Discussion Notebooks

  • NetworkX
  • Probability Concepts
  • Bayes Rule Example

Reminder that this week (and future week) discussions will not be like week 1! The participation due date will be during discussion.

  • Supplementary Notes on Probability
  • Week 2 Discussion Slides