CS6140 Machine Learning

Syllabus (subject to change)

Created: Mon 2 May 2005
Last modified:

	Date		Topic	Reading	Assignments
1	Mon	Sep 13	Administrivia Basic concepts	Background Probabilities Convexity1 , Convexity2 Linear algebra	student information required
2			Decision trees	DHS 8.1-8.5 Tom Mitchell lecture

3	Mon	Sep 20	Linear Regression
4			Logistic Regression		hw1 out

5	Mon	Sep 27	Generative models Multivariate analysis	DHS CH2, CH3 Andrew Ng - Generative Models
6			Bayesian networks	Bishop - graphical inference slides	project introduction

7	Mon	Oct 4	Perceptrons Neural networks	DHS CH6
8			Neural networks Evaluation of ML algorithms Good ML advice		hw1 due hw2 out

	Mon	Oct 11	COLUMBUS DAY NO CLASS

9	Mon	Oct 18	Linear discrimination	DHS ch 5
10			Convex optimization Support vector machines	Alex Smola introduction Cris Burges tutorial Cristianini slides DTREG tutorial, examples

11	Mon	Oct 25	Support vector machines		hw2 due, hw3 out
12			Kernels	Bernard Scholkopf tutorial	project check 1

13	Mon	Nov 1	Ensemble classifiers, Boosting	DHS chapter 9 Adaboost talk Adaboost paper Adaboost proof Rankboost paper
14			Boosting, Active learning

15	Mon	Nov 8	Dimensionality reduction PCA, Fisher Linear Discriminant	DHS chapter 3
16			Feature selection

17	Mon	Nov 15	Mixture models EM algorithm	EM paper 1 EM paper 2	hw3 due, hw4 out
18			Hidden Markov models	DHS chapter 3

19	Mon	Nov 22	Learning Theory Multiclass using Error Correcting Output Codes	PAC learning VC dimmension ECOC paper
20			Clustering, k-Means	DHS chapter 10

21	Mon	Nov 29	k Nearest neighbors Learning with harmonic functions
22			Clustering
			Collaborative filtering


23	Mon	Dec 6	MDL		hw4 due
24			Student presentations

25	Mon	Dec 13	Student presentations		project due
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