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Statistical Learning Theory, Part I

Sumio Watanabe

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

Prerequisite Knowledge : Linear algebra, defferentiation, integration, probability and statistcs are necessary.

For everyone who wants to take Statistical Learning Theory (MCS.T403)

Statistical learning theory is a subject that requires linear algebra,
differentiation, integration, probability, and statistics as prerequisite knowledge.
For this reason, an examination will be conducted to confirm basic knowledge
in the first lecture (from 9:00 on June 13), and only successful applicants may
apply for credits. Please note that those who are absent for the first time or
who have forgotten writing materials can not take the exam.

(1) Basic concepts in Statistical Learning Theory

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

(2) Neural Newtwork

Lecture Note 2 ,

(3) Neural Network

Lecture Note 3 ,

(4) Boltzmann Machine

Lecture Note 4 , Associative Memory, mp4 file .

(5) Deep Learning

Lecture Note 5 ,

Learning in Neural Network ,

Autoencoder, mp4 file ,

Convolution Neural Network, mp4 file ,

(6) Entropy and Information

Lecture Note 6 , Learning Curve, mp4 file ,

(7) Learning and Generalization

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

(8) Knowledge Discovery

Lecture Note 8 ,

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

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