Statistical Learning Theory, Part I


Sumio Watanabe


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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


(2) Gradient Descent
Lecture Note 2 , Steepest Descent, mp4 file , Accelerater, mp4 file , Stochastic, mp4 file


(3) Neural Network
Lecture Note 3 , Learning Process, mp4 file , Ridge and Lasso, mp4 file


(4) Boltzmann Machine
Lecture Note 4 , Associative Memory, mp4 file .


(5) Deep Learning
Lecture Note 5 ,
Parameter Space in Neural Network, mp4 file ,
Sequential Learning, mp4 file ,
Autoencoder, mp4 file ,
Convolution Neural Network, mp4 file ,


(6) Entropy and Information
Lecture Note 6 , Generalization Errors, mp4 file ,


(7) Learning and Generalization
Lecture Note 7 , Generalization loss and information criteria in Neural Networks, mp4 file ,


(8) Knowledge Discovery and Free Energy
Lecture Note 8 ,
(Realizable Case) Model Selection by AIC and BIC, mp4 file ,
(Unrealizable Case) Model Selection by AIC and BIC, mp4 file ,