The following is a tentative schedule of topics that will be considered in our weekly meetings. In each weekly assignment, I will itemize the readings that are required and will frequently suggest optional readings.

• Week of 2/4 Introduction to Machine Learning; Linear Models and Perceptrons
  • Assignment writeup
  • Required Readings
    Alpaydin, Chapter 1: Introduction
    Alpaydin, Chapter 2: Supervised Learning
    Alpaydin, Chapter 10: Linear Discrimination, Sections 10.1, 10.3, and 10.4 only
    Alpaydin, Chapter 11: Perceptrons, Sections 11.1-11.4 only
    Fawcett and Provost, "Combining Data Mining and Machine Learning for Effective User Profiling", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press, 1996.
    
  • Recommended Readings
    Duda, Hart, and Stork, Chapter 1: Introduction
    Mitchell, Chapter 1: Introduction
    Mitchell, Chapter 4: Artificial Neural Networks, Sections 4.1-4.4 only
    
• Week of 2/11 Probabilistic Models: Naive Bayes
• Assignment writeup
• Readings
  Chapter 3: Bayesian Decision Theory (up to page 55)
  Mitchell: Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression
  Mitchell: Section 3.4 2, page 59 only
  Witten and Frank: Section 4.2
  Duda, Hart, and Stork: Section 2.1
• Week of 2/18 Regression
• Assignment writeup
• Readings
  Sections 4.1, 4.2, 4.5
  Sections 4.6 and 5.8
  Frank et al, "Naive Bayes for Regression", Machine Learning, Vol. 41, No. 1, pages 5-25.
• Week of 2/25 Neural Networks
• Assignment writeup
• Readings
  Mitchell: Chapter 4 (at least through Section 4.6.2)
  Witten and Frank: pages 227-233
  Alpaydin: Chapter 11.1-11.9
  "Multitask Learning", Machine Learning, 1997.
• Week of 3/3 Support Vector Machines
• Assignment writeup
• Required Readings
  Alpaydin, Sections 10.2 and 10.9
  Alpaydin, Section 8.2
  "Support vector machines", from IEEE Intelligent Systems, July/August 1998.
  
• Optional Readings
  "A Tutorial on Support Vector Machines for Pattern Recognition" by Christopher Burges, Data Mining and Knowledge Discovery, June 1998
  "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines", by John Platt, April 1998.
  Course notes by Andrew Ng at Stanford
  Tutorial Notes on "Support Vector and Kernel Machines" by Nello Cristianini, ICML 2001, Williams College
  
• Application Readings
  ``Text Categorization with Support Vector Machines: Learning with Many Relevant Features'', T. Joachims. ECML-98, Tenth European Conference on Machine Learning, pp. 137--142.
  ``Breast Cancer Survival and Chemotherapy: A Support Vector Machine Analysis'', Y.-J. Lee, O. L. Mangasarian, and W. H. Wolberg. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 55:1--20, 2000.
  ``Classifying LEP Data with Support Vector Algorithms'', P. Vannerem, K.-R. M\"{u}ller, B. Sch\"{o}lkopf, A. Smola, S. S\"{o}ldner-Rembold. Procs. AIHENP99.
• Week of 3/10 Decision Trees
• Assignment writeup
• Readings
  Sections 9.1-9.3
  Mitchell: Chapter 3
  Russell and Norvig: Section 18.3
  Quinlan, "Induction of Decision Trees", Machine Learning, Vol. 1, No. 1., 1986.
  Page and Ray "Skewing: An Efficient Alternative to Lookahead for Decision Tree Induction", IJCAI-03
• Week of 3/31 K-Nearest Neighbor
  [Assignment takes into consideration that students will be off-campus during Spring Break]
• Assignment writeup
• Readings
  Mitchell: pages 230-236
  Russell and Norvig: pages 733-735
  Witten and Frank: pages 78-79 and 128-135
  Mitchell: pages 236-238
  "Locally Weighted Regression", Artificial Intelligence Review, 1997.
• Week of 4/7 Evaluation Methodology
  [Note that this week all meetings will be shifted to Monday and Tuesday.]
• Assignment writeup
• Readings
  Mitchell: Chapter 5
  Alpaydin: Chapter 14
  "The Case Against Accuracy Estimation for Comparing Induction Algorithms", ICML-98.
• Week of 4/14 Learning Theory
• Assignment writeup
• Readings
  Mitchell: Chapter 7 up to page 220
  Kearns and Vazirani: pages 22-24
  "A Short Introduction to Boosting" by Freund and Schapire, 1999.
• Week of 4/21 Bias/Variance Theory and Ensemble Methods
• Assignment writeup
• Readings
  Alpaydin: Sections 15.1, 15.2, 15.4, 15.5; also Sections 4.3 and 4.7
  Duda, Hart, and Stork: Sections 9.5.1 and 9.5.2
  "A Comparison of Decision Tree Ensemble Creation Techniques" by Banfield, Hall, Bowyer, and Kegelmeyer, IEEE PAMI, 2007. This is available in electronic form from our library.
  "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization" by Dietterich, Machine Learning Journal, 2000.
• Week of 4/28 Clustering
• Assignment writeup
• Readings
  "LOF: Identifying Density-Based Local Outliers" by Breunig et al., ACM SIGMOD, 2000
  "Density-Based Clustering in Spatial Databases: GDBSCAN and its Applications", Sander et al., Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998.
  Mitchell, Section 6.12
  Alpaydin, pages 140-144
  Witten and Frank, pages 137-138, 262-266, 337-338
  Bishop, 187-190, 65-72.
• Week of 5/5 Students' Choice
• Assignment writeup