CS 464 Introduction to Machine Learning

Fall 2012

Course Description:

This course provides a broad introduction to machine learning, the study of computing systems that improve their performance with experience. The primary focus of the course will be on understanding the basic learning algorithms and their applications to data mining problems. We will discuss applications of machine learning to data mining: predicting stock market returns, detecting fraudulent transactions, classifying microarray samples, and text classification.

Moodle page of the course:

Check regularly the Moodle page of the course for lecture notes, homework assignments, and announcements.

Textbooks:

• Tom Mitchell, Machine Learning, McGraw Hill, 1997 (required).

• Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann, 2011 (recommended).

Course Outline:

• Introduction

• Hypotheses spaces and concept learning

• Decision tree learning

• Artificial neural networks

• Experimental evaluation of learning algorithms

• Bayesian learning

• Instance-based learning

• Support vector machines

• Boosting and bagging

• Text categorization

• Clustering

• Genetic algorithms

Course requirements:

• There will be in-class quizzes with advance notice, homework assignments, two midterm exams and one project.

• Check regularly the Moodle page of the course for lecture notes, homework assignments, and announcements.