Special Interest Group on Machine Learning for Computer Graphics

[ Goal | Members | Resource | Time | Location | Contents ]

Goal:

Study several machine learning techiniques and ideas, especially for CG applications.

Members:

Jin Huang, Ying Tang, Rui Wang, Dong Xu, Hongxin Zhang, Xiangjun Zhao, Chao Song, Hua Liu, Dong Zhou... everyone is welcome.

Resource:

• Textbooks:
  • Information Theory, Inference, and Learning Algorithms , by David MacKay. This book is available for onscreen viewing online.
  • Machine Learning by Tom Mitchell, McGraw-Hill.
  • Pattern Classification (2nd ed.) by Richard O. Duda, Peter E. Hart and David G. Stork, Wiley Interscience, 680 pages. ISBN: 0-471-05669-3
  • Pattern recognition (2nd ed.) by S. Theodoridis and K. Koutroumbas.
• Links:
  • Machine Learning for Computer Graphics course by Hertzman, Machine Learning course  index table.
  • Bayesian inference in dynamic models.
  • Conference, NIPS, ICML 2004

Time:

2:00 PM, every Wednesday

Location:

Meeting Room

Contents:

Topic	Reporter	Time	Slides and Refenece
Introduction	Zhang Hongxin	Feb.25	Machine learning for Computer Graphics: a brief introduction
Fundemental knowledge of Machine learning	Xu Dong	Mar.05	Concept Learning
Bayes Method (Part 1)	Tang Ying	Mar.10	Bayesian Probabilistic reasoning and learning Additional reading Sections 2.1-2.3, 22.1 in the Mackay's book. The invited paper by Hertzman in Pacific Graphics 2003
Decision Tree and boosting	Wang Rui	Mar.17	Decision Tree and boosting
Feature Generation: Linear Transforms	Song Chao and Zhang Hongxin	Mar.24	1. Feature Generation: Linear Transforms 2. Pre-computing Additional reading: Chapter 6 of PR Book. A tutorial on Principal Components Analysis .
Working for Chinagraph		Mar.31/Apr.07
Instance Based Learning	Xu Dong	Apr.14	Instance Based Learning
Numerical methods	Xu Dong	Apr.21	Numerical Solvers for BVPs Additional reading: painless conjugate gradient multigrid method
Hidden Markov Model	Wang Rui	May. 26	Introduction to Hidden Markov Model
Bayes Method (Part 2)	Huang Jin	June. 2	Bayesian Probabilistic reasoning and learning (2) Additional reading: Bayessian Matting Poisson Matting
Clustering Methods	Zhang Hongxin
Monte Carlo Method	Huang Jin
Texture Synthesis	Tang Ying
Learning Sets of Rules (Part 2)	Wang Rui
Genetic Algorithms	Xu Dong
Dimension Reduction	Huang Jin