CS 464 Introduction to Machine Learning

Summer 2015

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 also cover examples from recent applications of machine learning to text mining.

Moodle page of the course:

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

Prerequisites:

• Knowledge of probability, statistics, linear algebra, algorithms and data structures, and good programming background (Java, C++, Matlab, R, etc.)

Textbook:

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

Additional Course Material:

• Kevin P. Murphy, Machine Learning: a Probabilistic Perspective, The MIT Press, 2012 (recommended).

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

• Ethem Alpaydin, Introduction to Machine Learning, The MIT Press, 2010.

Course Outline:

• Introduction

• Hypotheses spaces and concept learning

• Decision tree learning

• Artificial neural networks

• Bayesian learning

• Instance-based learning

• Genetic algorithms

• Support vector machines

• Boosting and bagging

• Text categorization

• Clustering

Course requirements:

• There will be several pop-up quizzes, homework assignments involving programming and discussion, one midterm exam and one project.

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