| WARNING: Small changes to this syllabus may be made during the semester. |
COURSE DESCRIPTION:Machine learning is concerned with the design and study of computer programs that are able to improve their own performance with experience, or in other words, computer programs that learn. In this graduate course we cover several theoretical and practical aspects of machine learning. We study different machine learning techniques/paradigms, including decision trees, neural networks, genetic algorithms, Bayesian learning, rule learning, and reinforcement learning. We discuss applications of these techniques to problems in data analysis, knowledge discovery and data mining.We will closely follow the excellent 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. CLASS MEETING:
Time: Tuesdays and Thursdays 1:00-2:20 pm INSTRUCTOR:
Prof. Carolina Ruiz TEXTBOOK:
PREREQUISITE:CS 534 or equivalent, or permission of the instructor. GRADES:
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. CLASS PARTICIPATIONStudents are expected to read the material assigned for each class in advance and to participate in class discussions. Class participation will be taken into account when deciding students' final grades.PROJECTS AND ASSIGNMENTSThere will be a total of 8 individual projects. Each assignment/project will be related to the topic covered during the corresponding week. They include implementation projects, assigned readings, and theoretical problems.For most of the projects, you can choose any of the two systems:
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. CLASS MAILING LISTThe mailing list for this class is: This mailing list reaches the professor and all the students in the class. CLASS WEB PAGESThe webpages for this class are located at http://www.cs.wpi.edu/~cs539/s11/Announcements will be posted on the web pages and/or the class mailing list, and so you are urged to check your email and the class web pages frequently. WARNING:Small changes to this syllabus may be made during the course of the semester.ADDITIONAL SUGGESTED REFERENCESSee my list of additional Machine Learning, AI, Data Mining, Statistics, Databases, Data Sets and other online resources.OTHER AI/ML RESOURCES ONLINE:
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