CS 534 - Machine Learning
Spring 2008

Overview

This course provides a broad introduction to machine learning and data mining. Topics include: supervised learning (discriminative/generative learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction); learning theory (bias/variance tradeoffs; VC theory; large margins); ensemble learning (bagging, boosting). If time allows, we will also cover sequential learning problems and algorithms. Lectures will discuss general issues in these topics and well-established algorithms, both from a computational aspect (how to compute the answer) and a statistical aspect (how to ensure that future predictions are accurate).

Learning Objectives of the Course:

1. Be able to formulate machine learning problems corresponding to different applications. 

2. Understand a range of machine learning algorithms along with their strengths and weaknesses. 

3. Understand the basic theory underlying machine learning. 

4. Be able to aply machine learning algorithms to solve problems of moderate complexity.

5. Be able to read current research papers and understand the issues raised by current research.

Exams

There will be two exams:
Exam I: Friday, May 9
Final exam: TBA

Assignments

The assignments in this course will consist of written problem sets and experimental assignments.

Grades

The final grade will be calculated as follows: midterm 20%, final 30%, final project 20%, problem sets and mini-projects 30%. 

Honor Code

Collaboration on assignments problems is permitted; copying of solutions or code is not. The work you hand in should be your own. Students should indicate on their homework, the names of all collaborators. While some students find studying together to be quite beneficial and enjoyable, I strongly encourage you to attempt to solve homework problems on your own first, as this is the only way to ensure that you have mastered the material. Generating solutions is much different than verifying solutions.

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