| Date | Optional Reading | Topic | Videos | Assignments | Due Date |
|---|---|---|---|---|---|
| Jan 8 | Ch. 1 | Introduction to AI | Introduction | P0:Tutorial | Jan 11 |
| Jan 10 | Ch. 3.1-4 | Uninformed Search | Uninformed Search SBS-1 | ||
| Jan 15 | Ch. 3.5-6 | A* Search and Heuristics | Informed Search SBS-2 | HW1: Search | Jan 21 |
| Jan 17 | Ch. 6.1 | Constraint Satisfaction Problems I | Constraint Satisfaction Problems I | P1: Search | Jan 25 |
| Jan 22 | Ch. 6.2-5 | Constraint Satisfaction Problems II | Constraint Satisfaction Problems II | HW2: CSPs | Jan 29 |
| Jan 24 | Ch. 5.2-5 | Game Trees: Minimax | Adversarial Search SBS-3 | ||
| Jan 29 | Ch. 5.2-5, 16.1-16.3 | Game Trees: Expectimax, Utilities | Expectimax | HW3: Games | Feb 5 |
| Jan 31 | Ch. 17.1-3 | Markov Decision Processes | MDPs | P2: Multi-Agent Pacman | Feb 8 |
| Feb 5 | Ch. 17.1-3 | Markov Decision Processes II | MDPS II | HW4: MDPs | Feb 14 |
| Feb 7 | Ch. 21 | Reinforcement Learning | Reinforcement Learning I | ||
| Feb 12 | Ch. 21 | Reinforcement Learning II | Reinforcement Learning II | HW5: RL P3: Reinforcement Learning | Feb 19 Feb 22 |
| Feb 14 | Catch-up Day | ||||
| Feb 19 | Catch-up Day | ||||
| Feb 21 | Ch. 13.1-5 | Probability | Probability | ||
| Feb 26 | Ch. 15.2,5 | Markov Models | Markov Models | ||
| Feb 28 | Midterm Exam | ||||
| Mar 5 | No Class (Polytechnic Faculty Candidate) | ||||
| Mar 7 | Ch. 14.1-2,4 | Bayes' Nets: Representation | Bayes Nets | HW6:Probabilities & Bayes' Nets | Mar 14 |
| Mar 12 | Ch. 14.1-2,4 | Bayes' Nets: Independence | Bayes Nets II SBS-4 | ||
| Mar 14 | Ch. 14.4 | Bayes' Nets: Inference | Bayes Nets Inference SBS-5 SBS-6 | HW7: Bayes' Nets: Inference, Sampling | Mar 28 |
| Mar 26 | Ch. 14.4-5 | Bayes' Nets: Sampling | Bayes Nets IV: Sampling SBS-7 | ||
| Mar 28 | Ch. 16.5-6 | Decision Diagrams / VPI | Decision Diagrams / VPI | HW8: Decision Diagrams, VPI | Apr 5 |
| Apr 2 | HMMs | Hidden Markov Models | |||
| Apr 4 | Applications of HMMs | HMM Applications | HW9: Particle Filtering & Naive Bayes | Apr 12 | |
| Apr 9 | Ch. 20.1-20.2.2 | ML: Naive Bayes | ML: Naive Bayes SBS-8 SBS-9 | P4: Ghostbusters | Apr 16 |
| Apr 11 | Catch-up Day | ||||
| Apr 16 | Ch. 18.6.3 | ML: Perceptrons | ML Perceptron SBS-10 | ||
| Apr 18 | Ch. 18.8 | ML: Optimization and Neural Networks | Optimization and Neural Nets | HW10: ML: Perceptrons HW11: ML: Optimization | Apr 19 Apr 23 |
| Apr 23 | ML: Neural Nets and Decision Trees | Neural Nets and Decision Trees | P5: Classification | April 26 | |
| Apr 25 | Review | ||||
| May 3 | Final Exam @ 10am |
The schedule is subject to change. The final is Friday, May 3, from 10:00 am - 12:00 pm in our normal classroom. The final is comprehensive.
