Introduction to AI
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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 7 - More Coding (Viterbi's & Likelihood Weighting), Expectation Maximization
Lecture Materials
Monday No Lecture
Week 7 Discussion
Discussion Slides
Wednesday Lecture:
Wednesday Lecture Notebook
Wedneday Participation Notebook
Friday Lecture (Will possibly livecast on twitch. Recording will be posted)
Friday Lecture Notebook & Participation