CSC 4510/9010: Machine Learning
Spring, 2015
Dr. Paula Matuszek
Adjunct Professor, Villanova
E-mail: Paula.Matuszek@villanova.edu or Paula.Matuszek@gmail.com
Phone: (610) 647-9789
TA: Kambagiri Atte, katte@villanova.edu

Description: Machine learning is often characterized as enabling behavior from the computer without explicitly programming it, by giving it examples or feedback instead. The computer then looks for patterns which can explain or predict what happens.

People study machine learning for several reasons:

There is also overlap between machine learning and data mining. Many techniques, such as classification and clustering, have grown out of both fields and differ more in history than in tools used.

This course will be a hands-on overview of machine learning. We will cover an introduction to supervised, unsupervised and other forms of machine learning, and apply techniques using Weka, a well-known, open source ML tool.

Format and Requirements: The course will be a combination of lectures, in-class activities, and team/group discussions. We will make extensive use of Weka, applying its tools to machine learning problems. Grading will be based on assignments, a midterm, and a final. In addition, each student will work with a team to complete a project applying machine learning to a domain question. 9010 students will also choose and present a paper on a specific machine learning topic or project. For more detail see class links below.

Text:
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition. Ian H. Witten , Eibe Frank , Mark A. Hall. Morgan Kaufmann, 2011
ISBN-10: 0123748569
ISBN-13: 978-0123748560

Syllabus
Requirements and Grading for 4510
Requirements and Grading for 9010
Academic Integrity
Student Questionnaire

I will be on campus primarily to teach class; I can meet with you before or after class, or by arrangement at other times. Email is the best way to reach me.