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Please use this identifier to cite or link to this item: http://hdl.handle.net/10117/7218

Title: o Class Meeting
Authors: WPI Computer Science
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Publisher: WPI Computer Science
Citation: http://www.cs.wpi.edu/~cs539/s03/
Abstract: WPI Worcester Polytechnic Institute Computer Science Department ------------------------------------------ CS539 Machine Learning Syllabus - Spring 2003 PROF. CAROLINA RUIZ WARNING: Small changes to this syllabus may be made during the course of the semester. ------------------------------------------ * Course Description * Class Meeting * Instructor * Textbook * Recommended Background * Class Schedule * Grades * Exam * Projects o Project 1: Using the Weka System to Preprocess Datasets - Due Monday, January 20 2003 at 11 am. o Project 2: Decision Trees - Due Monday, January 27 2003 at 8 am. o Project 3: Neural Networks - Part 1 Due Monday, February 03 2003 at 8 am. Part 2 Due Monday, February 10 2003 at 8 am. o Project 4: Evaluating Hypotheses - Due Monday, February 17 2003 at 11 am. o Project 5: Bayesian Learning - Due Monday, March 10 2003 at 8 am. o Project 6: Instance-Based Learning and Regression Methods - Due Thursday, March 20 2003 at 8 am. o Project 7: Genetic Algorithms - Due Thursday, April 03 2003 at 10 am. o Project 8: Rule Learning - Due Monday, April 14 2003 at 10 am. * Class Participation * Class Mailing List * Class Web Pages * Additional Recommended References * Warning * Other AI Resources Online 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 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. CLASS MEETING: Time: Mondays and Thursdays 11:00 am to 12:20 pm Room: FL311 Students are also encouraged to attend the KDDRG Seminar Fridays at 2:00 pm. INSTRUCTOR: Prof. Carolina Ruiz ruiz@cs.wpi.edu 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. TEXTBOOK: * Required: Tom M. Mitchell "Machine Learning" McGraw-Hill 1997. ISBN: 0070428077 * Recommended: o Pat Langley "Elements of Machine Learning" Morgan Kaufmann Publishers, Inc. 1995. ISBN 1-55860-301-8 o S. Russell, P. Norvig. "Artificial Intelligence: A Modern Approach". Prentice Hall, Second Edition, 2002. ISBN 0-13-790395-2 * Several additional readings will be handed out during the semester. PREREQUISITE: CS 534 or equivalent, or permission of the instructor. GRADES: Exam 20% 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. EXAMS There will be a final exam. The exam will cover the material covered in class since the beginning of the semester. PROJECTS AND ASSIGNMENTS There will be a total of 10 projects. Each assignment/project will be related to the topic covered during the corresponding week. They include implementation projects, assigned readings, theoretical problems, and individual project assignments. 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. CLASS PARTICIPATION Students 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. CLASS MAILING LIST The mailing list for this class is: cs539@cs.wpi.edu If your email address does not belong to the class mailing list, you can subscribe to it by sending the following one-line email message to majordomo@cs.wpi.edu: subscribe cs539 CLASS WEB PAGES The webpages for this class are located at http://www.cs.wpi.edu/~cs539/s03/ 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. ADDITIONAL SUGGESTED REFERENCES Machine Learning 1. Tom M. Mitchell. "Machine Learning" McGraw-Hill, 1997. 2. P. Langley. "Elements of Machine Learning" Morgan Kaufmann Publishers, Inc. 1996. 3. Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy, eds. "Advances in Knowledge Discovery and Data Mining" The MIT Press, 1995 4. See http://www.aic.nrl.navy.mil/~aha/research/ml/books.html for an extensive list of ML books organized by topics. General AI 1. T. Dean, J. Allen, Y. Aloimonos. "Artificial Intelligence: Theory and Practice" The Benjamin/Cummings Publishing Company, Inc. 1995. 2. B. L. Webber, N. J. Nilsson, eds. "Readings in Artificial Intelligence" Tioga Publishing Company, 1981. 3. Patrick H. Winston. "Artificial Intelligence" 3rd edition Addison Wesley. 4. S. L. Tanimoto. "The Elements of Artificial Intelligence Using Common Lisp" Computer Science Press 1990. 5. E. Rich and K. Knight. "Artificial Intelligence" Second edition McGraw Hill 1991. 6. P. Norvig. "Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp" Morgan Kaufmann Publishers, 1992. 7. M. Ginsberg. "Essentials of Artificial Intelligence" Morgan Kaufmann Publishers, 1993. 8. G. F. Luger and W. A. Stubblefield. "Artificial Intelligence Structures and Strategies for Complex Problem Solving" Third edition Addison-Wesley, 1998. 9. M.R. Genesereth and N. Nilsson. "Logical Foundations of Artificial Intelligence" Morgan Kaufmann, 1987. WARNING: Small changes to this syllabus may be made during the course of the semester. OTHER AI/ML RESOURCES ONLINE: * My list of Machine Learning and KDD Online Resources * Online Machine Learning Resources * Machine Learning Resources * Machine Learning Papers * UCI Machine Learning * Reinforcement Learning and Friends at Carnegie Mellon * Data Sets o Time Series Data Library o Data Repositories o Datasets for Data Mining * General AI o CMU Artificial Intelligence Repository o AI Journals o Challenge Problems for Artificial Intelligence
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