## Teaching in 2011## Convex Optimization and Machine Learning, Kyoto U. (Fall 2011)
Introduction with Matlab code to visualize Unit balls using different norms.
Assignment 1 ## Introduction to Information Sciences, Kyoto U. (Fall 2011)Here are the slides 1st lecture on information theory and communications primer movie by C.& R. Eames.
## Foundations of Intelligent Systems, Kyoto U. (Spring 2011)## Part I, Statistical Machine LearningApril 19th - June 7th Lecture 1 : Regression (Data: mat or csv file, Scripts) 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 June 12th (Sun.)## Homework 2, due July 16th (Sat.)## Part II, Computational Learning TheoryJune 14th - July 26th (taught by Prof. Yamamoto)
## Language Information Processing, Kyoto U. (Spring 2011)## Chapter on Machine Learning ApproachesJune 6th - June 20th Lecture 1 : Naive Bayes, SVM Lecture 2 : Conditional Random Fields Lecture 3 : Topic Models
## AssignmentYou can choose either of these two tasks: Write a reading report on Topic Models by D. Blei and J. Lafferty, 2009. Implement a simple prototype of CRF’s for shallow parsing based on Shallow Parsing using CRF’s by F. Sha and F. Pereira, 2004.
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## Guest Lecture in Prof. Avis’ Introduction to Algorithms and Informatics course |