CS6140 13F: Syllabus

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

1. Lecture 01, Wed, Sep 04 2013
   • Admistrivia
   • Introduction to Machine Learning

   • Reading: Murphy Chap. 1; DHS Chap. 1

2. Lecture 02, Mon, Sep 09 2013
   • Probability primer

   • Homework 01 assigned
   • Reading: Murphy Chap. 2; DHS Chap. A.4

3. Lecture 03, Wed, Sep 11 2013
   • Finish probability primer
   • Bayesian methods
   • Parameter estimation

   • Reading: Murphy Chap. 3; DHS Chap. 2

4. Lecture 04, Mon, Sep 16 2013
   • Finish Bayesian methods
   • Finish parameter estimation

   • Reading: Murphy Chap. 3; DHS Chap. 2

5. Lecture 05, Wed, Sep 18 2013
   • Linear and logistic regression
     • Gradient descent

   • Homework 01 due
   • Homework 02 assigned
   • Reading: Murphy Chap. 7, 8; DHS Chap. 9.6, 5, 6

6. Lecture 06, Mon, Sep 23 2013
   • Finish regression: logisitic regression vs. naive bayes
   • Performance analysis

   • Reading: Mitchell Naive Bayes and Logistic Regression; ROC curve demo; Murphy Chap. 8, 5.7.2; DHS Chap. 5, 6, 2.8.3

7. Lecture 07, Wed, Sep 25 2013
   • Perceptrons and neural networks

   • Homework 03 due
   • Reading: Murphy 8.5.4, 16.5; DHS Chap. 6

8. Lecture 08, Mon, Sep 30 2013
   • Finish neural networks: back propogation

   • Reading: Murphy 8.5.4, 16.5; DHS Chap. 6

9. Lecture 09, Wed, Oct 02 2013
   • Information Theory primer
   • Intro to decision trees

   • Homework 02 due
   • Homework 03 assigned
   • Reading: Murphy 2.8, 16.2; DHS Chap. A.7, 8

10. Lecture 10, Mon, Oct 07 2013
    • Decision trees
    • Reading: Virgil Pavlu's decision tree notes; Murphy 16.2; DHS Chap. 8

11. Lecture 11, Wed, Oct 09 2013
    • Bias and variance
    • Start ensemble methods: bagging, boosting

    • Homework 03 due
    • Reading: AdaBoost; Murphy 16.2.5, 16.4; DHS Chap. 9.3, 9.5

12. Lecture 12, Wed, Oct 16 2013
    • Finish boosting

    • Homework 04 assigned
    • Reading: Murphy 16.4; DHS Chap. 9.5

13. Lecture 13, Mon, Oct 21 2013
    • On-line learning:
      • halving algorithm
      • randomized halving algorithm
      • weighted majority algorithm

14. Lecture 14, Wed, Oct 23 2013
    • Finish on-line learning:
      • hedge algorithm

    • Homework 04 due (Fri)

15. To be announced...

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