Foundations of Machine
Instructor: Mehryar Mohri
TA: Ashish Rastogi
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:
Note: except from a few common topics only briefly addressed in
G22.2565-001, the material covered by these two courses have no
- Probability and general bounds
- PAC model
- Perceptron, Winnow
- Support vector machines (SVMs)
- Kernel methods
- Decision trees
- 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.
Familiarity with basics in linear algebra, probability, and analysis
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.
- Lecture 01: Introduction to machine learning, probability
- 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
- 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
- Lecture 13: Reinforcement learning
- Lecture 14: Empirical evaluation
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.
- Michael J. Kearns and Umesh V. Vazirani.
An Introduction to Computational Learning Theory.
MIT Press, 1994.
- Tom M. Mitchell.
- Bernhard Schoelkopf and Alex Smola.
Learning with Kernels.
MIT Press, Cambridge, MA, 2002.
- Vladimir N. Vapnik.
The Nature of Statistical Learning Theory.
- Vladimir N. Vapnik.
Statistical Learning Theory.
An extensive list of recommended papers for further reading is
provided in the lecture slides.