6.036 Introduction to Machine Learning (Spring 2015)
Tentative schedule of lectures:
- Lecture 1 (Tue 2/3):
Introduction
- Lecture 2 (Thu 2/5):
Linear classifiers, separability, perceptron algorithm
- Lecture 3 (Tue 2/10):
On-line classifiers, passive-agressive
- Lecture 4 (Thu 2/12):
Training objectives, over-fitting, regularization
- Monday schedule of classes (Tue 2/17)
- Lecture 5 (Thu 2/19):
Linear regression
- Lecture 6 (Tue 2/24):
Clustering, criteria, k-means
- Lecture 7 (Thu 2/26):
Clustering cont'd
- Lecture 8 (Tue 3/3):
Recommender problems, collaborative filtering
- Lecture 9 (Thu 3/5):
Non-linear classification, kernels
- Lecture 10 (Tue 3/10):
Ensembles, boosting
- Lecture 11 (Thu 3/12):
Neural networks, deep learning
- Lecture 12 (Tue 3/17):
deep learning, backpropagation
- MIDTERM EXAM (Thu 3/19): in class, 10-250
- Spring break (Tue 3/24)
- Spring break (Thu 3/26)
- Lecture 13 (Tue 3/31):
Generalization, model selection
- Lecture 14 (Thu 4/2):
Complexity, VC dimension
- Lecture 15 (Tue 4/7):
Generative models, mixtures
- Lecture 16 (Thu 4/9):
Mixtures and the EM algorithm
- Lecture 17 (Tue 4/14):
Representation of probability models: Bayesian networks
- Lecture 18 (Thu 4/16):
Hidden Markov Models: modeling
- Patriot's day vacation (Tue 4/21)
- Lecture 19 (Thu 4/23):
Hidden Markov Models: algorithms
- Lecture 20 (Tue 4/28):
Learning to control: Reinforcement learning
- Lecture 21 (Thu 4/30):
Reinforcement learning cont'd
- Lecture 22 (Tue 5/5):
Applications: robotics (or tbd)
- Lecture 23 (Thu 5/7):
Applications: vision (or tbd)
- Lecture 24 (Tue 5/12):
Applications: natural language processing (or tbd)
- Lecture 25 (Thu 5/14):
Exam review, wrap-up