Course Description:
Provides broad introduction to machine learning, including learning theory, and recent topics like support vector machines and feature selection. Covers basic ideas, intuition, and understanding behind modern machine learning methods. Discusses applications like face recognition, text recognition, biometrics, bioinformatics, and multimedia retrieval.Prerequisite:
- C or higher in CS 3358: Data Structures
Course Objectives:
|
1. |
To study how to build computer systems that learn from experience, including discussions of the major approaches currently being investigated. |
|
2. |
Understanding the underlying algorithms used in various learning systems. |
|
3. |
Students will have a thorough exposure to the methodologies, technologies, mathematics and alogrithms currently needed in machine learning, and they can apply these learning techniques to a target problem (e.g., learning to classify objects, to predict medical diagnoses and to recognize people, etc.) |
Course Notes:
None.
Section Info:
Lecture/Lab Hours: 3 hours lecture, 0 hours lab
Offered: 1st offered Fall 2009