CS 269, Spring 2010: Topics in Advanced Machine Learning
Instructor: Zhuowen Tu and Yingnian Wu

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:
Week 1, Tuesday (March 30):  Introduction of the course Introduction
Week 1, Tuesday (April 1): Background introduction to discriminiative classifiers.
Classifier basics, VC dimension
Week 2, Tuesday (April 6): Basics about SVM classifier Support Vector Machines
Week 2, Thursday (April 8): Ensemble Classifiers
Empirical study of the performance of  popular classifiers
Ensemble classifiers: bagging, boosting
Project 1
Week 3, Tuesday (April 13) 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)
Week 4, Thursday (April 22) Semi-supervised Learning
Label propagation

Week 5, Tuesday (April 26) Semi-supervised Learning
Harmonic functions, spectrum clustering slides
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