Time: 4:00PM~5:50PM, Tuesday and Thursday
Location: Bolter Hall 5273

webpage: http://www.loni.ucla.edu/~ztu/courses/2013_CS_spring/cs269_spring_2013.html


We will go through some of the topics in machine learning covering (1) supervied learning: 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) unsupervised manifold/metric learning, (5) sparsity and low-rank, (6) hierarchical models (deep learning, active basis, and biology driven models).

Grading policy: Two small projects with a medium-size project.

	Covering topics:	Materials
Week 1, Tuesday (April 2):  Introduction of the course	Introduction	Slides
Week 1, Thursday (April 4): Background introduction to discriminiative classifiers.	Classifier basics, VC dimension	Slides
Reading Materials	Machine learning review articles (P. Domingos) Kernel Tricks (M. Jordan) Classifier empirical studies (R. Caruana and A. Niculesu-Mizil, ICML 2006) Classifers in high-dimensional data (Caruana et al., ICML 2008)
Week 2, Tuesday (April 9): Basics about SVM classifier	Support Vector Machines
Week 2, Thursday (April 11): Supervised learning	Empirical study of the performance of  popular classifiers Ensemble classifiers: bagging, boosting, random forests	Slides
Reading Materials	Pattern Classification (Chapter 4) (Duda) Semi-supervised Learning Survey (X. Zhu)
Project 1	Due date: April 25
Week 3, Tuesday (April 16) Structrual prediction	Label propagation, transductive learning, Multiple instance learning	Slides
Week 3, Thursday (April 18) Structrual prediction	PAC  theory, Active learning	Slides
Week 4, Tuesday (April 13) Semi-supervised Learning		Slides
Reading Materials	Multiple instance learning
Week 4, Thursday (April 25) Semi-supervised Learning	Stacking, Cascade models
Week 5, Tuesday (April 30)
Week 5, Thursday (May 2) Unsupervised Learning	Harmonic functions, spectrum clustering, Diffusion maps	Slides
Project 2	Due date: May 23
Week 6, Tuesday (May 7) Unsupervised Learning	Manifold learning, metric learning
Week 6, Thursday (May 9) Sparse coding	Sparse coding, Low-rank
Reading Materials	Diffusion Maps (Coifman) LLE (Roweis and Saul) ISOMAP (Tenenbaum et al.) Normalized Cuts (Shi et al.) Label Propagation (Zhu et al.) Laplacian Eigenmaps (Belkin and Niyogi)
Week 7, Tuesday (May 14) Sparse coding
Week 7, Thursday (May 16)	Dictionary learning
Week 8, Tuesday (May 21) Deep learning	RBM and deep belief network
Week 8, Thursday (May 23) Hierarchical models
Week 9, Tuesday (May 28) Big data
Week 9, Thursday (May 30)