CSC 4510/9010: Applied Machine Learning
Mendel G88, Fall, 2016
Dr. Paula Matuszek
Adjunct Professor, Villanova
Office:  Mendel 288.
E-mail: Paula.Matuszek@villanova.edu or Paula.Matuszek@gmail.com
Phone: (610) 647-9789
TA: Shiva Beesu, sbeesu@villanova.edu, Office Hours Weds, THurs 10-1.

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, or which can be observed in the data.

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.

The course objectives include:

Format and Requirements: The course will be a combination of lectures, assignments, in-class activities, and team/group discussions. We will make extensive use of Weka. Grading will be based on assignments, a midterm, a final, and a team project.  Each student will work with a team to complete a project applying machine learning to a domain question. 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

Schedule
Requirements and Grading for 4510
Requirements and Grading for 9010
Course Policies

I will be on campus primarily to teach class.  I will be in Mendel 288 from 1:00 to 2:00 Tues/Thurs.  You can also arrange to meet me other times.  Email is generally the best way to reach me.

Shiva will have office hours Weds and Thurs 10:00-1:00.