This MIT course was one of the many Machine Learning materials I went thru. to know in and out of ML ....

Machine Learning Course Description

This introductory course gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/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)