Spring 2005
Foundations of Machine
Learning
Instructor: Mehryar Mohri
TA: Chopra
Sumit
Mailing
List
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
Here is a list of recommended papers for further reading.
Homework assignments