Introduction to AI
Jupyter Notebook Export Tutorial
Week 1 - Introduction, Agents and Environments
Week 2 - Probability and Bayesian Networks
Week 3 - Advanced Probability, Bayesian Networks and D-Seperation
Week 4 - Bayesian Networks and Review
Week 5 – HMMs, Maximum Likelihood Estimation, EM Algorithm
Week 6 – Coding Viterbi's Algorithm, HMMs, Maximum Likelihood Estimation, EM Algorithm and Review
Week 7 – More Coding (Forward & Backward and Viterbi's)
Week 8 - Likelihood Weighting, Expectation Maximation and MonteCarlo Methods
Week 9 - Coding Practice and Intro to Reinforcement Learning
Week 10 - Reinforcement Learning
Light
Rust
Coal
Navy
Ayu
UCSD CSE150A Winter 2025
Week 9 – Coding Practice and Intro to Reinforcement Learning
Lecture Materials
Monday Lecture will continue from Friday's Lecture
Monday Coding Notebook
Week 9 Discussion
Discussion Slides