Resources

Date Lectures and Exercises Additional Resources
Week 1
Jan 10, 12
Introduction, AI Ethics

Week 2
Jan 17, 19
Search

Week 3
Jan 24, 26
Constraint Satisfaction Problems and Local Search

Week 4
Feb 7, 9
Adversarial Search, Utilities

Week 5
Feb 14, 16
Markov Decision Processes

Week 6
Feb 21, 23
Reinforcement Learning, Probability

Week 7
Feb 28, Mar 2
Bayes Nets: Representation, Independence

Week 8
Mar 7, 9
Bayes Nets: Inference, Midterm

  • The Berkeley course's past exams (with solutions)
Week 9
Mar 21, 23
Bayes Nets: Sampling, (Hidden) Markov Models

Week 10
Mar 28, 30
Particle Filters, Decision Networks


Week 11
Apr 4, 6
Naive Bayes, Perceptrons, Clustering


Week 12
Apr 11, 13
Guest Lecture, Deep Learning


Week 13
Apr 18, 20
Guest Lecture II, Conclusion