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Machine Learning >> Content Detail



Lecture Notes



Lecture Notes

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

Probabilistic inference

Guest lecture on collaborative filtering (PDF)

24Current problems in machine learning, wrap up

 








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