We will go through some of the topics in machine learning covering (1) ensemble-based discriminative learning (bagging, boosting, and random forest), (2) support vector machine (SVM) families (svm, structural svm, latent svm), (3) semi-supervised learning (transductive learning, graph Laplacian), (4) hierarchical learning (deep learning, active basis, and biology driven models)

Grading policy: Two small projects with a medium-size project or a presentation of a state-of-the-art paper.

	Covering topics:	Materials
Week 1, Tuesday (March 30):  Introduction of the course	Introduction	slides
Week 1, Tuesday (April 1): Background introduction to discriminiative classifiers.	Classifier basics, VC dimension	slides
Week 2, Tuesday (April 6): Basics about SVM classifier	Support Vector Machines	slides
Week 2, Thursday (April 8): Ensemble Classifiers	Empirical study of the performance of  popular classifiers Ensemble classifiers: bagging, boosting	slides Project 1
Week 3, Tuesday (April 13) Boosting	Boosting
Week 3, Thursday (April 15) Random forests	Random Forests
Week 4, Tuesday (April 20) Semi-supervised Learning	Semi-supervised learning, transductive learning	slides (X. Zhu) survey
Week 4, Thursday (April 22) Semi-supervised Learning	Label propagation
Week 5, Tuesday (April 26) Semi-supervised Learning	Harmonic functions, spectrum clustering	slides Project2
Week 5, Thursday (April 29) Semi-supervised Learning	PAC  theory
Week 6, Tuesday (May 4) Semi-supervised Learning	Diffusion maps	slides (Coifman)
Week 6, Thursday (May 6) Active Learning	Active learning	slides (Dasgupta)
Week 7, Tuesday (May 11)	Mixture model, ICA
Week 7, Thursday (May 13)	LASSO, sparsity
Week 8, Tuesday (May 18)	sparse coding
Week 8, Thursday (May 20)	RBM and deep belief network
Week 9, Tuesday (May 25) Graphical models	Bag of words model	slides Project 3