We will closely follow the excellent recent book "Machine Learning" by Tom M. Mitchell and will discuss several state of the art research articles. The course will provide substantial hands-on experience through several computer projects.
For the catalog description of this course see the WPI Graduate Catalog.
Time: Mondays and Thursdays 11:00 am to 12:20 pm
Students are also encouraged to attend the KDDRG Seminar Fridays at 2:00 pm.
Prof. Carolina Ruiz
Office: FL 232
Phone Number: (508) 831-5640
Office Hours: Mondays 2:00-3:00 pm, Thursdays 3:00-4:00 pm, or by appointment.
Other speakers may occasionally be invited to lecture to the class.
CS 534 or equivalent, or permission of the instructor.
|Weekly Assignments||80% (8% each)|
|Class Participation||Extra Points|
Your final grade will reflect your own work and achievements during the course. Any type of cheating will be penalized with an F grade for the course and will be reported to the WPI Judicial Board in accordance with the Academic Honesty Policy.
For most of the projects, we will use the Weka system (http://www.cs.waikato.ac.nz/ml/weka/). Weka is an excellent machine-leaning/data-mining environment. It provides a large collection of Java-based mining algorithms, data preprocessing filters, and experimentation capabilities. Weka is open source software issued under the GNU General Public License. For more information on the Weka system, to download the system and to get its documentation, look at Weka's webpage (http://www.cs.waikato.ac.nz/ml/weka/). You should download and use the 3.2.3 GUI version of the system.
More detailed descriptions of the assignments and projects will be posted to the course webpage at the appropriate times during the semester. An in-class presentation of each of the assignments will be required.