Resources

Links

Lectures

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No.
 Date
Title
References
1
 03/05 Introduction to Adaptive Filters and Machine Learning
2
 03/12 Linear Classification and Perceptron Learning Perceptron Learning
3
 03/19 Statistical Learning Theory Hoeffding
4
 03/26 Theory of Generalization Luxburg and Scholkopf
5
 04/02 Consistency, Bias-Variance Tradeoff and SRM  
6
 04/09 Regression  
04/16 No Class Today - Notes will be Posted Later  
04/23 Midterm Exam Week  
7
 04/30 Regularization, Validation, and Cross-Validation  
8
 05/14 Support Vector Machines and Kernels Burges (SVM Tutorial)
9
 05/21 Neural Networks
10
 05/21 Hidden Markov Models
11
 05/28 Radial Basis Function Networks
12
 05/28 SVM Applications
13
 06/11 Kalman Filter (Notes), (Lecture Slides)
14
 06/11 Boosting and Bagging
15
 06/11 Mixture of Gaussians
16
 06/11 Projects (Part I)
17
 06/17 Projects (Part II)

 

 

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