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

• Week of 2/22 Introduction to Machine Learning; Linear Models and Perceptrons
• Assignment writeup
• Required Readings
  Alpaydin (4th ed.), Chapter 1: Introduction
  Alpaydin (4th ed.), Chapter 2: Supervised Learning
  Alpaydin (4th ed.), Chapter 10: Linear Discrimination, Sections 10.1 and 10.3 only
  Alpaydin (4th ed.), Chapter 11: Perceptrons, Sections 11.1-11.4 only
  Ma, J., Saul, L.K., Savage, S., and Voelker, G.M., "Identifying Suspicious URLs: An Application of Large-Scale Online Learning", ICML 2009 Proceedings of the 26th International Conference on Machine Learning, ACM, pp 681-688.
  
• Recommended Readings
  Mitchell, Chapter 1: Introduction
  Mitchell, Chapter 4: Artificial Neural Networks, Sections 4.1-4.4 only
  
• Turn in your assignment here.
• Week of 3/1 Naive Bayes and Logistic Regression
• Assignment writeup
• Readings
  Alpaydin (3rd or 4th ed.), Chapter 3, Sections 3.1-3.4
  Mitchell: Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression through Section 2
  Mitchell (the actual textbook), Section 3.4 2, page 59 only
  Witten and Frank: Section 4.2
  Duda, Hart, and Stork: Section 2.1
  Mitchell: Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression, Section 3 through 3.1 only
  
• Turn in your assignment here.
• Week of 3/8 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
• Turn in your assignment here.
• Week of 3/15 k-Nearest Neighbor and Social Implications of Machine Learning
• Assignment writeup
• Readings (k-Nearest Neighbor)
  Mitchell: pages 230-236
  Russell and Norvig: pages 737-741
  Witten and Frank: pages 78-79 and 128-135
• Readings (Social Implications)
  Telford
  Savage
  Datta et al.
  Feldman et al.
  
• Turn in your assignment here.
• Week of 3/22 Artificial Neural Networks and Deep Learning
[Note that Tuesday's meetings will be rescheduled due to Reading Period.]
• Assignment writeup
• Readings
  Mitchell: Chapter 4 (at least through Section 4.6.2)
  Witten and Frank: pages 227-233
  Caruana, "Multitask Learning", Machine Learning, 1997.
  the Deep Learning book
  
• Turn in your assignment here.
• Week of 3/29 Support Vector Machines
• Assignment writeup
• Required Readings
  Alpaydin, Section 10.2
  Alpaydin, Chapter 14 (through 14.6)
  
• Optional Readings
  "A Tutorial on Support Vector Machines for Pattern Recognition" by Christopher Burges, Data Mining and Knowledge Discovery, June 1998
  Course notes by Andrew Ng at Stanford
  Chapter 11, Section 5 of Daumé
  
• Turn in your assignment here.
• Week of 4/5 Evaluation Methodology
  
• Assignment writeup
• Readings
  Mitchell: Chapter 5
  Alpaydin: Chapter 20 (through 20.7) in the fourth edition or Chapter 19 (through 19.7) in the third edition
  
• Turn in your assignment here.
• Week of 4/12 Computational Learning Theory
• Assignment writeup and Problem Set from Mitchell, Chapter 7
• Readings
  Mitchell: Chapter 7 up to page 220
  "A Short Introduction to Boosting" by Freund and Schapire, 1999.
• Turn in your assignment here.
• Week of 4/19 4/21-22 are Health Days. No tutorial meetings this week. If you'd like, you might start thinking ahead to final projects.
• Week of 4/26 Bias/Variance Theory and Ensemble Methods
• Assignment writeup
• Readings
  Alpaydin: Sections 18.1-18.4 (4th edition) or Sections 17.1-17.4 (3rd edition)
  Alpaydin: Sections 18.6-18.7 (4th edition) or Sections 17.6-17.7 (3rd edition)
  Alpaydin Sections 4.3 and 4.7 (both editions)
  "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization" by Dietterich, Machine Learning Journal, 2000.
• Turn in your assignment here.
• Week of 5/3 Unsupervised Learning (including more on Deep Learning)
• Assignment writeup
• Readings
  "Building High-level Features Using Large Scale Unsupervised Learning" by Le et al., ICML, 2012
  Deep Learning by Goodfellow, Bengio, and Courville
  Mitchell, Section 6.12
  Alpaydin, Sections 7.1-7.4
  Witten and Frank, pages 137-138, 262-266, 337-338.
• Turn in your assignment here.
• Week of 5/10 Student Projects: Proposals
• Assignment writeup
• Turn in your assignment here.
• Week of 5/17 Student Projects: Presentations
• Assignment writeup
• Turn in your assignment here.