Time: 11:10AM - 12:25PM

Claxton 205

Fall 2007

Machine Learning home page | Syllabus | Schedule/Readings | Project Assignments | Resources |

** Course Description: **
Machine Learning is the study of how to build computer systems that learn from experience.
This CS494/594 course on *Machine Learning* will explain how to build systems
that learn and adapt using real-world applications (such
as robotics and brain wave signal understanding).
Some of the topics to be covered include reinforcement learning,
neural networks, genetic algorithms and genetic programming, parametric learning (density
estimation), clustering, and so forth. The course will be project-oriented, with emphasis placed
on writing software implementations of learning algorithms applied to real-world problems.

**Prerequisites:** Familiarity with basic concepts of
computer science (algorithms, data structures, and complexity),
mathematical maturity in discrete math (CS311), matrix math (Math 251),
probability and statistics (Math 323), and ability to program algorithms
in a language of your choice.

** Instructor:** Prof. Lynne E. Parker

*Office:*Claxton 220*Email:*parker at cs.utk.edu*Office Hours:*Tuesday/Thursday 12:30-1:30 PM (or send email to make appointment for another time)

** TA:** Rasko Pjesivac

*Office:*Claxton 124b*Email:*pjesivac at cs.utk.edu*Office Hours:*Monday 2:00-3:00, Wednesday 11:00-12:00 (or send email to make appointment for another time)

- Machine Learning, by Tom Mitchell, McGraw Hill, 1997 (should be available in UTK bookstore).