Course Announcement for Fall 2014
CSCE 478/878: Introduction to Machine Learning


Stephen Scott
Avery 268
sscott AT cse
Previous offering:

Meeting Time: 9:30–10:45 Tue/Thu in Avery 19

Track Classification: This course satisfies the Applications Track requirement for the graduate program in Computer Science. It also meets an Integrative Studies (IS) requirement for the BS degree, for students under the ES/IS system.

Credits: 3 units

Prerequisites: The only prerequisite is CSCE 310 or similar programming experience. STAT 380 (or equivalent) is useful, but not required (we will review all necessary material from probability and statistics).

Textbook: Introduction to Machine Learning, Second Edition by Ethem Alpaydin, MIT Press, 2009, ISBN 9780262012430. (Plus relevant supplemental materials.)

Optional Textbook: Machine Learning by Tom M. Mitchell, McGraw-Hill, 1997, ISBN-13 9780070428072.

Course Description:

Machine learning is a subarea of artificial intelligence (AI) that is concerned with the question of how to construct computer programs that automatically improve with experience. In recent years, many successful machine learning applications have been developed, including data mining programs that learn to detect fraudulent credit card transactions, information-filtering systems that learn users' reading preferences, face-recognition systems that learn to automatically identify people in images, biological sequence analysis programs that learn how to search massive databases for proteins belonging to certain families, and autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundations of this field.

The goal of this course is to present the key algorithms and theory that form the core of machine learning. Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity, algorithm design and analysis, and control theory. In this course we will review the field of machine learning in these contexts and work to understand the problem settings, algorithms, and assumptions that underlie each.

The topics the course will cover include decision trees, neural networks, support vector machines, hypothesis evaluation and ROC analysis, Bayesian learning, instance-based learning, and (time permitting) genetic algorithms and reinforcement learning. This course differs from the courses on neural networks, genetic algorithms, and artificial intelligence currently offered in that it gives a broad overview of machine learning rather than going deep into a single topic. At the end of the course, the students will sufficiently understand the fundamentals of these areas of machine learning to begin basic research in the area and to understand the ideas of technical papers in machine learning.

Grades in this course will be based on homework exercises, written exercises, and a project, in lieu of exams. For information on the previous offering of this course (Fall 2012), see

Last modified 15 August 2014; please report problems to sscott AT cse.