• Apr. 30 - The following is a paper for drawing ROC graphs.
  • Tom Fawcett (2003). ROC Graphs: Notes and Practical Considerations for Researchers. HP Labs Tech Report HPL-2003-4.
• Apr. 30 - The mid term exam is delivered to the class. The following are reference papers.
  • Caragea, D., Silvescu, A., and Honavar, V. (2001). Towards a theoretical framework for analysis and synthesis of agents that learn from distributed dynamic data sources. In Emerging Neural Architectures Based on Neuroscience, volume Invited Chapter. Berlin: Springer-Verlag.
  • Caragea, D., Silvescu, A., and Honavar, V. (2004). A framework for learning from distributed data using su±cient statistics and its application to learning decision trees. International Journal of Hybrid Intelligent Systems, 1 (2), 80-89.
• Apr. 30 - We will have make-up classes @ U702 during 1pm to 2pm Thursday on May 15. (We had classes on Apr. 10, Apr. 17., and Apr. 24)
• Apr. 23 - No class next week.
• Apr. 23 - Take-home midterm will be delivered to the students on 28 Apr. 2008. The due date will be 6 May 2008.
• Apr. 23 - Project suggestions are up. (Proposal deadline is 30 Apr. 2008).
  • Zhang, J.; Kang, D.; Silvescu, A. and Honavar, V. Learning Accurate and Concise Naive Bayes Classifiers from Attribute Value Taxonomies and Data Knowledge and Information Systems, 2006, 9.
  • Kang, D.; Silvescu, A. and Honavar, V. RNBL-MN: A Recursive Naive Bayes Learner for Sequence Classification 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), Springer Verlag, 2006, 3918.
  • Lafferty, J. D.; McCallum, A. and Pereira, F. C. N. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., 2001, 282-289.
  • Chevaleyre, Y. and Zucker, J. Solving Multiple-Instance and Multiple-Part Learning Problems with Decision Trees and Rule Sets. Application to the Mutagenesis Problem AI '01: Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence, Springer-Verlag, 2001, 204-214
  • Blockeel, H.; Page, D. and Srinivasan, A. Multi-instance tree learning ICML '05: Proceedings of the 22nd international conference on Machine learning, ACM, 2005, 57-64
  • Leiva, H. A. MRDTL: A multi-relational decision tree learning algorithm Iowa State University, 2002
  • Lachiche, N. and Flach, P. A. Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), AAAI Press, 2003, 416-423
  • Dai, W.; Yang, Q.; Xue, G. and Yu, Y. Boosting for transfer learning ICML '07: Proceedings of the 24th international conference on Machine learning, ACM, 2007, 193-200
• Apr. 14 - Homework 2 is up (deadline = 25 Apr. 2008).
  • Codrington, C. W. and Brodley, C. E. (1997). On the qualitative behavior of impurity-based splitting rules I: The minima-free property. Technical Report 97-05, Dept. of Computer Science, Cornell University.
  • Domingos, P. and Pazzani, M. J. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29 (2-3), 103-130.
  • Elomaa, T. and Rousu, J. (1997). On the well-behavedness of important attribute evaluation functions. In SCAI '97: Proceedings of the sixth Scandinavian conference on Artificial intelligence, (pp. 95-106)., Amsterdam, The Netherlands. IOS Press.
• Apr. 2 - The deadline for the project proposal is Apr. 18th.
• Apr. 2 - Lab Assignment 1 is posted with the following: (deadline = 16 Apr. 2008)
  • breast cancer data in ARFF format
  • house-votes-84.data in CSV format
  • Reuters in ZIP format
  • Kang et al.'s paper in SARA 2005
  • Eyheramendy's paper on Naive Bayes Model
• Mar. 18 - We will have a make-up class at 1pm on Mar. 19.
• Mar. 11 - Due to urgent project deadline, today's class is cancelled. We will discuss the time of a make-up class at the class next week (Mar. 18).
• Mar. 10 - Homework 1 is debugged and re-posted. (Thanks to Tan Hee Yuan.) Deadline is Mar. 25 before the class.
• Feb. 14 - Coming soon to the classroom near you!

• Course Background - Machine learning is a subfield of artificial intelligence.
• Course Description
• Course Objectives
• Course Prerequisites
• Target Audience
• Course Policies -

• Instructor - Dae-ki Kang, Ph.D.

• Lectures : Wednesday 12pm-3pm @ U702
• Office Hours : by appointment thru e-mail, or Monday 10am-5pm

• Primary Text - Bishop, C. (2006). Pattern Recognition and Machine Learning. Berlin: Springer-Verlag.
• Additional References
• Lecture Notes - Dr. Honavar's Machine Learning Study Guide
• Quizes, Homeworks, Tests - HW 1,Lab Assignment 1

Course Materials

• Syllabus
• Study Guide - Under Dr. Honavar's permission, we use his excellent machine learning study guide for this lecture.
• Problem Sets - HW 1, HW 2
• Lab Assignments - Lab Assignment 1
• Term Projects - Project Ideas proposal deadline is Apr. 18th.
• Examinations
• Grades