Lecture 1 (Tue 2/2): Introduction
Lecture 2 (Thu 2/4): Linear classifiers, separability, perceptron algorithm
Lecture 3 (Tue 2/9): On-line classifiers, passive-agressive
Lecture 4 (Thu 2/11): Maximum margin hyperplane, over-fitting, regularization
HW 1 Due (Fri 2/12)
Monday schedule of classes (Tue 2/16)
Lecture 5 (Thu 2/18): Linear regression
HW 2 Due (Fri 2/19)
Lecture 6 (Tue 2/23): Recommender problems, collaborative filtering
Lecture 7 (Thu 2/25): Non-linear classification, kernels
Project 1 Due (Fri 2/26)
Lecture 8 (Tue 3/1): Learning features, Neural networks
Lecture 9 (Thu 3/3): Deep learning, backpropagation
HW 3 Due (Fri 3/4)
Lecture 10 (Tue 3/8): Recurrent neural networks
Lecture 11 (Thu 3/10): Recurrent neural networks
HW 4 Due (Fri 3/11)
Lecture 12 (Tue 3/15): Deep learning architectures
MIDTERM EXAM (Thu 3/17): in class, 26-100
Spring break (Tue 3/22)
Spring break (Thu 3/24)
Lecture 13 (Tue 3/29): Generalization, complexity, VC-dimension
Lecture 14 (Thu 3/31): Clustering, criteria, k-means
Project 2 Due (Fri 4/1)
Lecture 15 (Tue 4/5): Generative models, mixtures
Lecture 16 (Thu 4/7): Mixtures and the EM algorithm
Lecture 17 (Tue 4/12): Representation of probability models: Bayesian networks
Lecture 18 (Thu 4/14): Hidden Markov Models: modeling
HW 5 Due (Fri 4/15)
Patriot's day vacation (Tue 4/19)
Lecture 19 (Thu 4/21): Hidden Markov Models: algorithms
Lecture 20 (Tue 4/26): Learning to control: Reinforcement learning
Lecture 21 (Thu 4/28): Reinforcement learning cont'd
Project 3 Due (Fri 4/29)
Lecture 22 (Tue 5/3): Applications: tbd
Lecture 23 (Thu 5/5): Applications: tbd
Lecture 24 (Tue 5/10): Applications: tbd
Lecture 25 (Thu 5/12): Exam review, wrap-up