CIS 526: MACHINE LEARNING
Time: Wednesday, 4:40-7:20pm; Place: Tuttleman 305 A
Instructor: Zoran Obradovic
303 Wachman Hall, email@example.com, phone: 215 204 6265
Office hours: Wednesday 2-3pm and by appointment
The goal of the field of machine learning is to build computer systems that learn from experience and that are capable to adapt to their environments. The course will cover the key algorithms and theory that form the core of machine learning. Machine learning draws on concepts from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity, and control theory. This course should provide a view from all of these perspectives and facilitate understanding of different problem settings, algorithms and assumptions that underlie each. The course will include individual projects to provide the students a better insight in the machine learning research and potentially attract them to pursue the graduate research in this area.
Stat503 or CIS511, undergraduate understanding of probability, statistics, linear algebra, calculus.
Primary textbook: Mitchell,T.M. Machine Learning, WCB/McGraw-Hill,1997,ISBN 0-07-042807-7.
Recommended book: Duda R.O., Hart P.E., Stork D.G. Pattern Recognition, Wiley & Sons, 2001, ISBN 0-471-05669-3
Conference and journal papers will be used as supplemental materials.
Topics:Content will include:
· Learning system design and evaluation
· Decision tree learning
· Neural networks, support vector machines and ensemble methods
· Unsupervised learning, clustering and density estimation
· Dimensionality reduction
· Bayesian networks
· Reinforcement learning.
Grading: Homework (30%), midterm exam (20%), reading/presenting assignments (20%) and an individual research project (30%).
Late Policy and Academic Honesty: No late submissions will be accepted. Discussing materials with fellow students is acceptable, but programs, experiments and the reports must be done individually.