Teaching in 2012

Foundations of Intelligent Systems, Kyoto U. (Spring 2012)

Part I, Statistical Machine Learning

April 17th - May 29th

• Introduction

• Lecture 1 : Regression (Data: mat or csv file, Scripts). Recommended reading for derivatives and gradients: Appendix A.4 of this book

• Lecture 2 : Regression 2

• Lecture 3 : Classification (Scripts, Perceptron Demo)

• Lecture 4 : Statistical Learning Theory 1 (Scripts)

• Lecture 5 : Statistical Learning Theory 2 (Scripts)

• Lecture 6 : Statistical Learning Theory 3

• Lecture 7 : SVM's and Introduction to Kernel Methods

Homework 1, Due May 14th 23:59 (by email).

Homework 2, Due June 18th 23:59 (by email).

Language Information Processing, Kyoto U. (Spring 2012)

Chapter on Machine Learning Approaches

June 4th - July 9th - July 16th

• Lecture 1 : Naive Bayes, Sparse Classifiers, SVM

• Lecture 2 : Conditional Random Fields

• Lecture 3 : Topic Models

Assignment

You can choose either of these two tasks:

• Write a reading report on Topic Models by D. Blei and J. Lafferty, 2009.

• Programming exercise to try topic models: Use an implementation of LDA (e.g. Gensim) to summarize a large corpus of text (e.g. the Reuters21578 database). Show how texts can be represented using LDA, highlight a few topics of interest as proposed in the paper above.

Please complete the assignment by July 30th, 23:59 and send it to me by email.

Please check here for more content on this course.

Introduction to Information Sciences, Kyoto U. (Fall 2012)

Here are the slides

• 1st lecture on formal languages

• 2nd lecture on formal languages

• 1st lecture on information theory and communications primer movie by C.& R. Eames.

• 2nd lecture on information theory

• 1st lecture on machine learning

• 2nd & 3rd lectures on machine learning

Please complete the assignment described in the 4th lecture by November 26th, 23:59 and send it to me by email, with the title “Assignment IIS”