### Course Details

Course description

This course provides an introduction to machine learning algorithms, which
learn from experience in a supervised, unsupervised, or reinforcement
learning paradigm. Specific algorithms include k-Nearest Neighbors,
Decision Trees, Support Vector Machines, Neural Networks, Bayesian Networks,
Clustering, Reinforcement learning, and Ensemble learning.

Course goals

At the end of the course, students are able to:

- Apply several machine learning algorithms (supervised and unsupervised) to data sets in standard format
- Select an appropriate algorithm to solve new problems
- Compare two algorithms in terms of concept/hypothesis representation, feature types, stability, and runtime complexity
- Evaluate learning systems using cross-validation and statistical significance tests
- Use the Weka machine learning library (Java)

Prerequisites

- Data structures and algorithms (CS 203, 312)
- Calculus and Propositional logic
- Recommended: Probability (Math 474) and Statistics (Math 274)

### Vital Statistics

Time and place

Credits: 4

Lectures: Saturdays, 9:10 a.m. - 1:00 p.m., ET-A210

Lectures: Saturdays, 9:10 a.m. - 1:00 p.m., ET-A210

Instructor: Kiri Wagstaff

Office hours: Saturdays, 1:00-1:30 and 2:00-3:00 p.m., ET-A210

Email: wkiri@wkiri.com

Course website: http://www.wkiri.com/cs461-w09/

Textbook

*Introduction to Machine Learning*by Ethem Alpaydin, MIT Press, 2004.

*Machine Learning*by Tom Mitchell, McGraw Hill, 1997.

Exams

**Midterm**: February 7, 2009, during lecture- There is no final exam (final project instead)