Machine Learning Tutorial, KAIST | MLT 2013

Distances and Kernels in Machine Learning

[Slides]

A simple Matlab GUI to illustrate metric learning with ITML or LMNN and 3 labels.

Pattern Recognition Advanced, Kyoto U. (Spring 2013)

SVM, CRF's and Topic Models

July 3 - July 17

• SVM & Kernels [Slides]

• Naive Bayes, Logistic regression, Conditional Random Fields [Slides]

• Topic Models [Slides]

• Homework 1: due July 10 10:30 AM, either in paper form in the course mailbox or sent to this this email. What is “kernel ridge regression”? Can you use it for binary classification? Describe succintely this tool in 1 page.

• Homework 2: due July 17 10:30 AM, either in paper form in the course mailbox or sent to this this email. What is the relationship between logistic regression and maximum entropy models? Summarize this relationship in 1 page. This document and others you may find on the web may be useful.

Introduction to Information Sciences, Kyoto U. (Fall 2013)

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

Vietnam National University @ Ho-chi-minh, Optimization and Kernel Methods (06/2010)

• Introduction to the course

• Introduction to Optimization, Linear Programming

• Duality, Convex Programming

• SVM's, regression, kernel methods introduction

EPAT ML school, Kernel Methods (05/2010)

• Lecture 1 (4 hours) Kernels, mathematical definitions

• Lecture 2 (2 hours) Methods

• Bibliographic keys used in the slides above.