Schedule

Date		Topic	Readings	Deadlines
01/18	Wed	Introduction to AI	Ch 1
01/20	Fri	Rational Agents states, actions, optimality, rationality	Ch 2
01/23	Mon	Search Algorithms transition model, cost function, search tree	Ch 3.1-3
01/25	Wed	Uninformed Search best-first, breadth-first, depth-first, iterative-deepening	Ch 3.3-4	Project 0 (Pre-Reqs)
01/30	Mon	Informed Search uniform-cost, a-star, heuristic, admissibility, consistency	Ch 3.5-6
02/01	Wed	Constraint Satisfaction scheduling, constraint graph, backtracking	Ch 6.1-5
02/06	Mon	Review
02/08	Wed	Adversarial Search zero-sum, mini-max, alpha-beta pruning	Ch 5	Problem Set 1
02/13	Mon	Markov Decision Processes policy, reward, transition, utility, discount	Ch 17.1	Project 1 (Search)
02/15	Wed	Solving MDPs 1 value, q-value, value iteration, bellman equation	Ch 17.2
02/20	Mon	Solving MDPs 2 policy iteration	Ch 17.2
02/22	Wed	Passive Reinforcement Learning bandit problems, temporal-difference learning, q-learning	Ch 22.2
02/27	Mon	Active Reinforcement Learning exploration vs exploitation, epsilon-greedy, exploration fn	Ch 22.3
03/01	Wed	Representing Uncertainty joint, marginal, and conditional probability	Ch 12.1-12.3	Problem Set 2
03/06	Mon	Bayes’ Rule chain rule, product rule, inference, independence	Ch 12.4-12.6
03/08	Wed	Markov Models POMDPs, belief state, markov chain	Ch 17.4, 14.1	Project 2 (MDPs)
– Spring Break –
03/27	Mon	Exact Inference in Temporal Models HMMs, filtering, state estimation, forward algorithm	Ch 14.2
03/29	Wed	Approx. Inference in Temporal Models particle filtering, sampling	Ch 14.4-14.5
04/03	Mon	Multivariate Inference dynamic bayes net, forward-backward, viterbi algorithm	Ch 14.3
04/05	Wed	Naive Bayes classification, feature vector	Ch 12.6, 19.6	Problem Set 3
04/10	Mon	Support Vector Machines decision boundary, kernel	Ch 19.7
04/12	Wed	Perceptrons loss, back-propagation, gradient descent	Ch 21.1	Project 3 (Inference)
04/17	Mon	Multi-layer Perceptrons hidden layer, activation function, softmax, convolution	Ch 21.2
04/19	Wed	Graduate Student Presentations advanced applications of AI
04/24	Mon	Graduate Student Presentations advanced applications of AI		Problem Set 4
04/26	Wed	Graduate Student Presentations advanced applications of AI
05/01	Mon	No class reading period		Project 4 (Neural Nets)