Prof. Dan Simovici, Office hours: M 2:00-4:00, W 2:00
-3:00; other time can be arranged individually.
Machine
Learning is a foundational discipline for data mining and artificial
intelligence grounded in probability theory and linear algebra. This course is intended to develop a context
for placing various algorithms and techniques developed in subsequent
applied courses, especially in data mining.
Concepts,
Hypotheses, Learning Algorithms (one week)
The Vapnik-Chervonenkis Dimension (2 weeks)
Boolean
Formulas and Representation of Hypothesis Spaces ( one week)
Probabilistic
Learning (3 weeks)
Complexity of
Learning Algorithms (3 weeks)
Linear
Threshold Networks (1 week)
Parametric
Training Methods(2 weeks)
Support
Vector Machines (2 weeks)
Students will
be evaluated based on their homework , a take-home midterm and a take-home final examination. All students are invited to attend the data
mining seminar (Mondays at 5:30, starting February 4th 2013).
The main reference for the course are the lectures. Slides used in the lectures and other
handouts will be posted below.
In addition we recommend:
1.
M. Anthony and N. Biggs: Computational Learning Theory, Cambridge University Press,
Cambridge, UK, 1997
2.
M. J. Kearns and U. V. Vazirani: An Introduction to Computational Learning
Theory, The MIT Press, Cambridge, MA, 1997