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Statistical Learning Theory, Part I

Sumio Watanabe

Lecture in Department of Mathematical and Computing Science, Tokyo Institute of Technology.

(1) Basic concepts in Statistical Learning Theory

Lecture Note 1 , Classification by a neural network, mp4 file ,
MATLAB file

(2) Gradient Descent

Lecture Note 2 , Steepest Descent, mp4 file ,
Accelerater, mp4 file ,
Stochastic, mp4 file ,
MATLAB file

(3) Neural Network

Lecture Note 3 , Ridge and Lasso, mp4 file ,
Training Data ,
Test Data ,
Graph Drawing ,
MATLAB file

(4) Boltzmann Machine

Lecture Note 4 , Associative Memory, mp4 file ,
Training Data ,
MATLAB file

(5) Deep Learning

Lecture Note 5 , Sequential Learning, mp4 file ,
Convolutional Neural Network, mp4 file ,

(Sequential Learning)
MATLAB file 1 , MATLAB file 2 ,
Training Data , Test Data ,

(Convolutional Neural Network)
MATLAB file 3 , MATLAB file 4 ,
Training Data

(6) Entropy and Information

Lecture Note 6 , KL information of Learning Process, mp4 file ,
MATLAB file

(7) Learning and Generalization

Lecture Note 7 ,
Normal mixture K=3, mp4 file ,
Normal mixture K=2, mp4 file ,
Normal mixture K=5, mp4 file ,
MATLAB file

(8) Knowledge Discovery and Free Energy

Lecture Note 8 ,

(Realizable Case) Model Selection by AIC and BIC, mp4 file , MATLAB file

(Unrealizable Case) Model Selection by AIC and BIC, mp4 file , MATLAB file