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 4 – Bayesian Networks and Review
Homework 2 - Due 11:59 PM Sunday, Feb 9
Check canvas assignments for files.
Lecture Materials
Monday Lecture Slides
Monday Lecture Participation
Monday Discussion Slides
Solutions & Notes
Friday Lecture Slides
PolyTree's Markov Blankets and NoisyOR
Naive Bayes
Friday Lecture Participation