Teaching in 2013
Machine Learning Tutorial, KAIST | MLT 2013
Distances and Kernels in Machine Learning
A simple Matlab GUI to illustrate metric learning with ITML or LMNN and 3 labels.
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Foundations of Intelligent Systems, Kyoto U. (Spring 2013)
Part I, Statistical Machine Learning
April 16th - May 28th
Classification [VIDEO] [Slides] [Scripts] [Perceptron Demo]
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Recommended reading for derivatives and gradients: Appendix A.4 of this book
Homework 1, due May 7th (Tue) noon, either in paper form in the course mailbox or sent to this this email.
Homework 2, due May 21st (Tue) noon. Please send your homework to this email.
Homework 3, due July 9th (Tue) noon. Please send your homework to this email.
Teaching Assistant: Tam Le
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 information theory and communications primer movie by C.& R. Eames.
Large Scale Optimization and Machine Learning, Kyoto U. (Fall 2013)
Recommended reading to take the course
Sections A.1, A.2, A.3, A.5.1, A.5.2, C.1, C.2 in Convex Optimization.
Slides:
Introduction with Matlab code to visualize Unit balls using different norms.