Course Description

This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications. Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services. The main topics covered are:

• Probability and general bounds
• PAC model
• VC-dimension
• Perceptron, Winnow
• Support vector machines (SVMs)
• Kernel methods
• Decision trees
• Boosting
• Regression problems and algorithms
• Ranking problems and algorithms
• Halving algorithm, weighted majority algorithm, mistake bounds
• Learning automata, Angluin-type algorithms
• Reinforcement learning, Markov decision processes (MDPs)

Location and Time

Room 101 Warren Weaver Hall,
251 Mercer Street.
Tuesdays 5:00 PM - 6:50 PM.

Prerequisite

Familiarity with basics in linear algebra, probability, and analysis of algorithms.

Projects and Assignments

There will be roughly 4 assignments and a project. The final grade is essentially the average of the assignment and project grades. The standard high level of integrity is expected from all students, as with all CS courses.

Lectures

• Lecture 01: Introduction to machine learning, probability review
• Lecture 02: PAC model, sample complexity for finite hypothesis space, general bounds and inequalities
• Lecture 03: VC dimension
• Lecture 04: Support vector machines
• Lecture 05: Support vector machines, kernel methods
• Lecture 06: Perceptron algorithm, quadratic optimization algorithms
• Lecture 07: Decision trees
• Lecture 08: Boosting, on-line learning algorithms
• Lecture 09: Multi-class classification algorithms
• Lecture 10: Regression problems and algorithms
• Lecture 11: Ranking problems and algorithms
• Lecture 12: Learning automata, Angluin-type algorithms
• Lecture 13: Reinforcement learning
• Lecture 14: Empirical evaluation

Textbooks

There is no single textbook covering the material presented in this course. Here is a list of books recommended for further reading:

• Luc Devroye, Laszlo Gyorfi, Gabor Lugosi. A Probabilistic Theory of Pattern Recognition. Springer, 1996.
• Michael J. Kearns and Umesh V. Vazirani. An Introduction to Computational Learning Theory. MIT Press, 1994.
• Tom M. Mitchell. Machine learning. McGraw-Hill, 1997.
• Bernhard Schoelkopf and Alex Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
• Vladimir N. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.
• Vladimir N. Vapnik. Statistical Learning Theory. Wiley, 1998.

Technical Papers

An extensive list of recommended papers for further reading is provided in the lecture slides.

Homework

• Homework 1 [solution].
• Homework 2 [solution].
• Homework 3 [solution].
• Homework 4 [solution].