
Lecture  Topic  Reading/Assignments/Notes  Due Date 
L1  Introduction to Machine Learning 
Read the introductory chapter of [M], [WF], [F] or [A]
See also lecture slides. 

Supervised Learning Basics:  
L2  Instance based learning 
[M] Chapter 8 is cloest to class material;
or [RN] 18.8; or [DHS] 4.44.6.
See also lecture slides. See also Andrew Moore's tutorial on kdtrees See also original paper describing the Relief Method 

L34  Decision Trees 
[M] Chapter 3;
or [RN] 18.14; or [F] Chapter 5.
See also lecture slides. 

Optional Reading  T. Dietterich, M. Kearns, and Y. Mansour Decision Tree Learning and Boosting Applying the Weak Learning Framework to Understand and Improve C4.5. International Conference on Machine Learning, 1996.  
Written Assignment 1  Assignment 1  9/24  
Empirical/Programming Assignment 1  Project 1 and corresponding Data  9/29  
L5  Naive Bayes Algorithm 
[M] 6.16.2, and 6.96.10;
[DHS] Section 2.9;
[F] 9.2; [WF] 4.2.
See also new book chapter from [M] See also lecture slides. Lecture also provided a basic introduction to probability and working with random variables. 

L67  Evaluating Machine Learning Outcomes 
[M] Ch 5; [F] Ch 12
See also lecture slides. 

Optional Reading 
Foster Provost, Tom Fawcett, Ron Kohavi
The Case Against Accuracy Estimation for Comparing Induction
Algorithms
Proc. 15th International Conf. on
Machine Learning, 1998.
T. Dietterich, Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms Neural Computation 10(7), 1998. Stephen Salzberg On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach Data Mining and Knowledge Discovery, 1997. 

L78  Features (selection, transformation, discretization) 
Relevant reading includes some portions of [F] Chapter 10, and [A]
Chapter 6
(there is a good overlap but not a perfect match)
See also lecture slides. 

Optional Reading 
Wrappers for Feature Subset Selection
Ron Kohavi, George H. John
Artificial Intelligence, 1996.
(Read till section 3.2 inclusive.)
Supervised and unsupervised discretization of continuous features. James Dougherty, Ron Kohavi, and Mehran Sahami. International Conference on Machine Learning, 1995. 

Written Assignment 2  Assignment 2  10/8  
Empirical/Programming Assignment 2  Project 2 and corresponding Data  10/13  
L810  Linear Threshold Units 
[M] 4.14.4; DHS 5.5;
See also
new book chapter from [M]
See also lecture slides. 

Written Assignment 3  Assignment 3  10/20  
Thursday 10/22  Midterm Exam 
Material for the exam includes everything covered up to October 13
(all the material above this point in the table).
Everything discussed in class is included for the exam.
The reading assignments are supporting materials that should be useful in review and study but I will not hold you responsible for details in the reading that were not discussed in class.
The Exam is closed book; no notes or books are allowed; no calculators or other machines of any sort are allowed. The exam will aim to test whether you have grasped the main concepts, problems, ideas, and algorithms that we have covered, including the intuition behind these. Generally speaking, the exam will not test your technical wizardry with overly long equations or calculations, but, on the other hand, it is sure to include some shorter ones. 

L11  Clustering 
[DHS] 10.67,10.9; [F] 8.45
See also lecture slides. 

L12L14  Unsupervised and SemiSupervised Learning with EM 
[M] Section 6.12; [A] 7.4; [F] 9.4; [DHS] 3.9
Text Classification Using Labeled and Unlabeled Documents using EM Nigam et. al, Machine Learning Volume 39, pages 103134, 2000. (The entire paper is relevant; you can skip section 5.3) See also lecture slides . 

Empirical/Programming Assignment 3  Project 3 and corresponding Data  11/5  
L15  Association Rules 
[F] 6.3; [WF] 4.5
Mining Association Rules between Sets of Items in Large Databases Rakesh Agrawal, Tomasz Imielinski, Arun Swami Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 1993. See also lecture slides. 

Optional Reading 
Real World Performance of Association Rule Algorithms
Zheng et al, KDD 2001.
Mining the Most Interesting Rules Bayardo et all, KDD 1999. Dynamic Itemset Counting and Implication Rules for Market Basket Data Brin et al, SIGMOD 1997. Discovering All Most Specific Sentences Gunopulos et al, TODS, 2003. 

L16L17  Computational learning theory 
[M] 7.1, 7.2, 7.3, 7.5, [RN] 18.5
and (for perceptron) [DHS] 5.5.2 or [CST] 2.1.1
Topics covered: online learning, the Perceptron convergence theorem, weighted majority, PAC learning, Agnostic PAC learning, learning conjunctions. 

Written Assignment 4  Assignment 4  11/19  
L18  Neural Networks 
[M] Chapter 4, [RN] 18.7, [DHS] 6.15.
See also lecture slides. 

L1921  Kernels, Dual Perceptron, Support Vector Machines 
[CST] pages: 919 and 2632, [RN] 18.9, [F] 7.3
A practical guide to support vector classification C.W. Hsu, C.C. Chang, C.J. Lin. Technical report, Department of Computer Science, National Taiwan University. July, 2003. See also lecture slides. 

Empirical/Programming Assignment 4  Project 4 and corresponding Data  12/3  
L22  Active Learning 
Active Learning Literature Survey
See also lecture slides. 

Optional Reading  The Robot Scientist Adam IEEE Computer (Volume:42, Issue: 8) 2009.  
Written Assignment 5  Assignment 5  12/10  
L2324  Overview of MDPs and Reinforcement Learning 
[RN] Sections 17.13 and Chapter 21; [M] 13.13
Topics covered: MDPs, Planning in MDPs: policies, policy evaluation (as linear equations), policy improvement, policy iteration algorithm, the Bellman equation, the values iteration algorithm. Bandit problems and the exploration exploitation problem. Model free algorithms in MDPs: policy evaluation: Monte Carlo value updates, temporal difference value updates. Model free planning: the SARSA algorithm. 

L25  Aggregation Methods 
[F] Chapter 11, [A] Chapter 17
Explaining AdaBoost In: Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, Springer, 2013. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Dietterich, T. Machine Learning, 40 (2) 139158, 2000. Useful information about Random Forests See also lecture slides. 

Optional Reading 
Boosting the margin: A new explanation for the effectiveness of voting methods.
Robert E. Schapire, Yoav Freund, Peter Bartlett and Wee Sun Lee.
The Annals of Statistics, 26(5):16511686, 1998.
(source of margin graphs in slides;
the introduction is informative and accessible)
Improved boosting algorithms using confidencerated predictions Robert E. Schapire and Yoram Singer Machine Learning, 37, 1999. (source of confidence rated Aadaboost version in slides) 

Tuesday, 12/15, 3:305:30  Final Exam 
Material for the exam includes everything covered during the semester (i.e., it is cumulative).
Everything discussed in class and homework assignments is included for the exam.
The reading assignments are supporting materials that should be useful in review and study but I will not hold you responsible for details in the reading that were not discussed in class.
The Exam is closed book; no notes or books are allowed; no calculators or other machines of any sort are allowed. The exam will aim to test whether you have grasped the main concepts, problems, ideas, and algorithms that we have covered, including the intuition behind these. Generally speaking, the exam will not test your technical wizardry with overly long equations or calculations, but, on the other hand, it is sure to include some shorter ones. 