6.036 Introduction to Machine Learning (Spring 2014)
Tentative schedule of lectures:
- Lecture 1 (Tue 2/4):
Introduction, linear classification
- Lecture 2 (Thu 2/6):
Linear classifiers, separability, perceptron algorithm
- Lecture 3 (Tue 2/11):
On-line classifiers, passive-agressive
- Lecture 4 (Thu 2/13):
Objective functions, regularization
- Monday schedule of classes (Tue 2/18)
- Lecture 5 (Thu 2/20):
Linear regression
- Lecture 6 (Tue 2/25):
Clustering 1
- Lecture 7 (Thu 2/27):
Clustering 2
- Lecture 8 (Tue 3/4):
Maximum margin classification, support vector machine
- Lecture 9 (Thu 3/6):
Non-linear classification, kernels
- Lecture 10 (Tue 3/11):
Ensembles, boosting
- Lecture 11 (Thu 3/13):
Generalization, model selection
- Lecture 12 (Tue 3/18):
Complexity, VC dimension
- MIDTERM EXAM (Thu 3/20): in class, 10-250
- Spring break (Tue 3/25)
- Spring break (Thu 3/27)
- Lecture 13 (Tue 4/1):
Recommender problems, collaborative filtering
- Lecture 14 (Thu 4/3):
Generative models, mixtures
- Lecture 15 (Tue 4/8):
Mixtures and the EM algorithm
- Lecture 16 (Thu 4/10):
Bayesian networks 1
- Lecture 17 (Tue 4/15):
Bayesian networks 2
- Lecture 18 (Thu 4/17):
Hidden Markov Models 1
- Patriot's day vacation (Tue 4/22)
- Lecture 19 (Thu 4/24):
Hidden Markov Models 2
- Lecture 20 (Tue 4/29):
Reinforcement learning 1
- Lecture 21 (Thu 5/1):
Reinforcement learning 2
- Lecture 22 (Tue 5/6):
Applications: robotics
- Lecture 23 (Thu 5/8):
Applications: vision
- Lecture 24 (Tue 5/13):
Applications: natural language processing
- Lecture 25 (Thu 5/15):
Review lecture