Teaching materials for machine learning

1. Introduction:

   Topic	Notes	Slides	Reading	Homework
   Probability Basics		Andrew Moore Rafal Bogacz Sam Roweis		Chris Williams Michael Jordan Kevin Murphy

2. Classification:

   Topic	Notes	Slides	Reading	Homework
   Naive Bayes	David Barber Kevin Murphy	Amos Storkey Kevin Murphy	Tom Mitchell	Kevin Murphy Amos Storkey
   Logistic Regression	David Barber	Amos Storkey Milos Hauskrecht	Tom Mitchell A. Ng and M. Jordan	Amos Storkey
   Decision Trees	Deniz Yuret	Carlos Guestrin Andrew Moore Rafal Bogacz	Nils Nilsson	Tom Mitchell
   Kernel Methods	Max Welling Romain Thibaux	Max Welling Schölkopf	Muller et al
   SVM	Andrew Ng Max Welling	Andrew Moore Carlos Guestrin (part 1) (part 2) Giovanni Maria Farinella	Chris Burges	Andrew Ng Tom Mitchell Tommi Jaakkola

3. Clustering:

   Topic	Notes	Slides	Reading	Homework
   Clustering	M. Girolami	Junming Yin Thorsten Joachims Tommi Jaakkola	Jain et al	Michael Jordan Tom Mitchell

4. Regression:

   Topic	Notes	Slides	Reading	Homework
   Regression	David Barber	Andrew Moore Romain Thibaux Chris Williams	Chris Williams	Kevin Murphy Lafferty and Wasserman: Excercises/Solutions
   Linear Regression	Kevin Murphy Mark Girolami	Tommi Jaakkola		Lafferty and Wasserman: Excercises/Solutions Tommi Jaakkola
   Gaussian Processes		Chris Williams	Chris Williams

5. Dimensionality reduction:

   Topic	Notes	Slides	Reading	Homework
   Dimensionality Reduction	David Barber Chris Williams	Zoubin Ghahramani Percy Liang	Roweis and Saul	Percy Liang Amos Storkey
   PCA	M. Girolami Andrew Ng Kevin Murphy	Amos Storkey	Lindsay Smith	Kevin Murphy

6. Model Selection:

   Topic	Notes	Slides	Reading	Homework
   Model Selection/Comparison	Andrew Ng	Zoubin Ghahramani

7. Parameter estimation/Optimization techniques

   Topic	Notes	Slides	Reading	Homework
   Parameter estimation	Michael Jordan Kevin Murphy	Kevin Murphy	Max Welling
   The EM Algorithm	Andrew Ng Max Welling Michael Jordan	Zoubin Ghahramani Tommi Jaakkola	Neal and Hinton	Kevin Murphy

8. Data Mining

   Topic	Notes	Slides	Reading	Homework
   Data mining	Association Rules	Amos Storkey

9. Ensemble learning methods

   Topic	Notes	Slides	Reading	Homework
   Ensemble learning methods	Thomas Dietterich	Carlos Guestrin Samy Bengio Tommi Jaakkola	Leo Breiman Robert Schapire Friedman et al

10. Evolutionary Computation:

    Topic	Notes	Slides	Reading	Homework
    Evolutionary Computation	Peter Ross	Gillian Hayes Melanie Mitchell	Darrel Whitley	Gillian Hayes Melanie Mitchell

11. Generative Methods:

    Topic	Notes	Slides	Reading	Homework
    Bayesian Networks	Kevin Murphy	Andrew Moore Chris Williams	D. Heckerman	Kevin Murphy Tom Mitchell
    Hidden Markov Models	Max Welling	Chris Williams Erik Sudderth Andrew Moore	L. R. Rabiner Ghahramani	Erik Sudderth
    Exact Inference & JTA	Kevin Murphy Michael Jordan	Chris Williams Tommi Jaakkola Kevin Murphy	Rina Dechter	Chris Williams
    Approximate Inference	Max Welling	Zoubin Ghahramani Kevin Murphy	Jordan et al Thomas Minka	Lafferty and Wasserman
    MCMC	Kristopher Beevers Michael Jordan Kevin Murphy	Zoubin Ghahramani Kevin Murphy	Andrieu et al	Kevin Murphy
    Markov Random Fields	Brani Vidakovic Kevin Murphy	Charles A. Bouman		Kevin Murphy

12. Learning theory

    Topic	Notes	Slides	Reading	Homework
    Statistical learning theory	Bousquet et al	Thorsten Joachims Samy Bengio
    PAC Learning	Rob Nowak David Haussler	Mary Cryan Andrew Moore Juris Viksna Kevin Murphy

13. Neural networks

    Topic	Notes	Slides	Reading	Homework
    Neural Networks	David Barber	Andrew Moore Carlos Guestrin (part 1) (part 2) Samy Bengio	Amos Storkey	Tom Mitchell

14. Reinforcement Learning / Q-learning

    Topic	Notes	Slides	Reading	Homework
    Reinforcement learning	Andrew Ng	Peter Bodik William Hsu (1) (2) Gillian Hayes Zoubin Ghahramani	Kaelbling et al	Peter Bodik Gillian Hayes Gillian Hayes

15. Significant Applications

    Topic	Notes	Slides	Reading	Homework
    Significant Applications		Rafal Bogacz	N. Friedman Lauritzen and Sheehan P. Perez

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© 2008 Robert Fisher