Lecture Notes

LEC # TOPICS
1 Introduction, linear classification, perceptron update rule ( PDF)
2 Perceptron convergence, generalization ( PDF)
3 Maximum margin classification ( PDF)
4 Classification errors, regularization, logistic regression ( PDF)
5 Linear regression, estimator bias and variance, active learning ( PDF)
6 Active learning (cont.), non-linear predictions, kernals ( PDF)
7 Kernal regression, kernels ( PDF)
8 Support vector machine (SVM) and kernels, kernel optimization ( PDF)
9 Model selection ( PDF)
10 Model selection criteria ( PDF)
11 Description length, feature selection ( PDF)
12 Combining classifiers, boosting ( PDF)
13 Boosting, margin, and complexity ( PDF)
14 Margin and generalization, mixture models ( PDF)
15 Mixtures and the expectation maximization (EM) algorithm ( PDF)
16 EM, regularization, clustering ( PDF)
17 Clustering ( PDF)
18 Spectral clustering, Markov models ( PDF)
19 Hidden Markov models (HMMs) ( PDF)
20 HMMs (cont.) ( PDF)
21 Bayesian networks ( PDF)
22 Learning Bayesian networks ( PDF)
23

Probabilistic inference

Guest lecture on collaborative filtering ( PDF)

24 Current problems in machine learning, wrap up

Course Info

Learning Resource Types

assignment_turned_in Problem Sets with Solutions
grading Exams with Solutions
notes Lecture Notes