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I. Search
- Aug 22
- Course overview
- slides, AIMA 1–2 (optional)
- HW0 out
- Aug 24
- Modeling search, backtracking search
- slides, goat.py, AIMA 3.1–3.3
- Aug 29
- BFS, DFS, UCS
- slides, goated.py, mazes, AIMA 3.4
- HW0 due HW1 out
- Aug 31
- Heuristic search, A*, problem relaxation
- slides, AIMA 3.5–3.6
- Sep 5
- Adversarial games, minimax, alpha-beta pruning
- slides, AIMA 5.1–5.2
II. Logic
- Sep 7
- Boolean algebra, propositional logic, syntax, semantics
- notes, AIMA 7.1–7.4
- Sep 12
- Model checking, inference rules, forward inference with modus ponens, Horn clauses
- notes, AIMA 7.4–7.5
- HW1 due HW2 out
- Sep 14
- Conjunctive normal form, inference by resolution
- notes, AIMA 7.5
- Sep 19
- First-order logic
- notes, AIMA 8
III. Constraint Satisfaction Problems
- Sep 21
- Factor graphs, assignment weights
- slides, AIMA 6.1
- Sep 26
- Backtracking search, heuristics, arc consistency, AC-3
- slides
- HW2 due HW3 out
- Sep 28
- Exam review
- Oct 3
- Midterm 1
IV. Probabilistic Graphical Models
- Oct 5
- Exam solutions
- midterm 1 histogram
- Oct 10
- From logic to probability, conditional probability, random variables
- notes, AIMA 12.1–12.2
- HW3 due
- Oct 12
- Joint distributions, marginalization, independence, Bayes’ rule
- notes, 12.3–12.5
- Oct 17
- Bayesian networks, conditional independence, explaining away, d-separation
- slides, AIMA 13.1–13.2
- HW4 out
- Oct 19
- Exact inference by enumeration, forward sampling
- slides, AIMA 13.3
- Oct 24
- Approximate inference, rejection sampling, importance sampling, likelihood weighting
- slides, sampling, AIMA 13.4
V. Machine Learning
- Oct 26
- Supervised learning, empirical risk minimization, decision trees
- slides, AIMA 19.1–19.3
- Oct 31
- Fitting decision trees, linear regresion
- slides, AIMA 19.3, 19.6
- HW4 due
- Nov 2
- Multiple linear regression, gradient descent
- slides, AIMA 19.6
- HW5 out
- Nov 7
- Training vs. testing, approximation vs. generalization, bias vs. variance
- slides
- Nov 9
- Midterm review
- Nov 14
- Midterm 2
- Nov 16
- Midterm solutions
- midterm 2 histogram
- HW5 due
- Nov 21
- –
- Nov 23
- –
- Nov 28
- Overfitting, regularization
- slides
VI. Deep Learning
- Nov 30
- Word embeddings, RNNs, transfer learning
- HW6 out
- Dec 5
- Transformers
- Dec 7
- Course conclusion
- HW6 due
- Dec 12
- Final Exam 10:15 am – 12:15 pm