CS6140 11F: Syllabus

Note: This is an approximate syllabus; it may change at any time.

1. Lecture 01, Wed, Sep 07 2011
   • Admistrivia
   • Introduction to Machine Learning

   • Reading: DHS Chap. 1, A.4

2. Lecture 02, Wed, Sep 14 2011
   • Probability primer

   • Homework 01 assigned
   • Reading: DHS Chap. A.4

3. Lecture 03, Wed, Sep 21 2011
   • Finish probability primer
   • Bayesian methods
   • Parameter estimation

   • Homework 01 due
   • Reading: DHS Chap. 2

4. Lecture 04, Wed, Sep 28 2011
   • Finish Bayesian methods
   • Finish parameter estimation
   • Start regression and logistic regression

   • Homework 02 assigned
   • Reading: DHS Chap. 2, 3, 5

5. Lecture 05, Wed, Oct 05 2011
   • Performance analysis
   • Training regression and logistic regression
     • Gradient descent

   • Reading: DHS Chap. 9.6, 5, 6

6. Lecture 06, Wed, Oct 12 2011
   • Finish regression, logistic regression, gradient descent
   • Perceptrons and neural networks

   • Homework 02 due (Fri Oct 14)
   • Reading: DHS Chap. 5, 6

7. Lecture 07, Wed, Oct 19 2011
   • Information Theory primer
   • Decision trees

   • Homework 03 assigned (Mon Oct 24)
   • Reading: DHS Chap. A.7, 8

8. Lecture 08, Wed, Oct 26 2011
   • Finish decision trees
   • Start ensemble methods: boosting, bagging

   • Reading: DHS Chap. 8, 9.5

9. Lecture 09, Wed, Nov 02 2011
   • Finish boosting
   • On-line learning:
     • halving algorithm
     • randomized halving algorithm
     • weighted majority algorithm

   • Homework 03 due

10. Lecture 10, Wed, Nov 09 2011
    • Finish on-line learning:
      • hedge algorithm
    • Multiclass prediction: ECOC

11. Lecture 11, Wed, Nov 16 2011
    • Machine learning theory: PAC learning

    • Homework 04 assigned

12. Lecture 12, Wed, Nov 30 2011
    • Finish machine learning theory: PAC learning
    • Nearest neighbor techniques
      • k-NN
      • collaborative filtering
    • MDL: the minimum description length principle

    • Homework 04 due
    • Reading: DHS Chap. 4.4-6, 9.2.3-4

13. Lecture 13, Wed, Dec 07 2011
    • Support Vector Machines
    • Kernel methods

    • Reading: DHS Chap. 5.11, ICML SVM Tutorial

14. Final Project
    • Due date: Tue, Dec 13 2011

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