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Winter School 2010


Indian Institute of Science Campus, Bangalore, India
January 9-17, 2010

Sponsored by
Canadian Institute for Advanced Research (CIFAR)
Microsoft Research India (MSR India)


William Freeman (MIT)

Sunita Sarawagi (IIT Bombay)

Brendan Frey (Toronto)

Manik Varma (MSR India)

Yann LeCun (NYU)

Martin Wainwright (Berkeley)

Jitendra Malik (Berkeley)

Yair Weiss (Hebrew University)

Bruno Olshaussen (Berkeley)

Richard Zemel (Toronto)

B. Ravindran (IIT Madras) 




Machine learning and its application to visual data processing is one of the most interesting, interdisciplinary and rapidly growing areas of research. The objective of this winter school was to equip graduate students, faculty members and outstanding undergraduate students with a variety of tools needed to conduct research in this area.

National and international leaders in the field (see the above list) gave introductory tutorials, advanced seminars and participated in small-group discussions.

Download slides and notes

Speaker  Talks/Tutorials Notes (coming soon) 
William Freeman 
  1. Where computer vision needs help from computer science (and machine learning)
  2. Applications of belief propagation in low-level vision (part1, part2, part3, part4)
  3. Removing blur due to camera shake from images (part1, part2, part3)
Brendan Frey 
  1. Introduction to Machine Learning
  2. Probability and Maximum Likelihood
  3. Bayesian Learning & Estimation Theory
  4. Pattern Classification & Decision Theory
  5. Neural Networks and Kernel Methods
  6. Clustering
  7. Learning generative models of images
Yann LeCun 
  1. Deep Learning (part1, part2, part3)
  2. Learning Invariant Feature Hierarchies (part1, part2)
  3. Other Methods and Applications of Deep Learning
Jitendra Malik 
  1. Classification using intersection kernel SVMs is efficient
  2. How should we combine high level and low level knowledge? (part1, part2)
  3. Poselets: Body Part Detectors trained Using 3D Human Pose Annotations
Bruno Olshaussen 
  1. What we know and don't know about biological vision
  2. Natural image statistics and visual coding (part1, part2, part3)
B. Ravindran 
  1. Reinforcement Learning - Learning from Interaction 
  2. Selective attention in Reinforcement Learning
Sunita Sarawagi 
  1. Graphical Models
  2. Structured learning
  3. Training algorithms for Structured Learning
  4. Querying for relations from the semi-structured Web
Manik Varma 
  1. Introduction to Machine Learning
  2. Multiple Kernel Learning

Martin Wainwright 

Yair Weiss 
  1. Belief Propagation and Linear Programming - theory and applications
  2. Semi-supervised learning in huge image collections
Richard Zemel 
  1. Learning to label complex images
  2. A Flexible Framework for Incorporating High-Order Constraints  



Jan-9 (morning)

Brendan Frey

ML - 101

Jan-9 (afternoon) 

Manik Varma

Support Vector Machines

Jan-10 (morning)

B. Ravindran

Reinforcement Learning

Jan-10 (afternoon) 

Sunita Sarawagi

Graphical Models 

Jan-11 (morning) 

Bruno Olshaussen

Tutorial: What we know and don't know about biological vision

Research: Natural image statistics and visual coding 

Jan-11 (afternoon) 

Yair Weiss

Tutorial: Theory and app of graph models

Research: Semi-supervised learning in huge image collections 

Jan-12 (morning) 

Brendan Frey

Tutorial: Learning generative models of images

Research: Image epitomes and applications 

Jan-12 (afternoon) 

Martin Wainwright

Tutorial: Graphical models and relaxations

Research: Model selection in high dimension 

Jan-13 (morning) 

William Freeman

Tutorial: BP in low-level vision

Research: Removing blur due to camera shake

Jan-13 (afternoon) 

Jitendra Malik

Tutorial 1: Graph Cuts, Regions and Contours

Research: How should we combine bottom-up and top-down information to understand images 

Jan-14 (morning) 

Manik Varma

Tutorial: Multiple kernel learning

Research: Max-Margin Multi-Label Classification

Jan-14 (afternoon) 

Yann LeCun

Tutorial: Deep learning

Research: Invariant feature extraction 

Jan-15 (morning) 

Sunita Sarawagi

Tutorial: Structured learning with applications to Information Extraction

Resarch: Efficient inference with cardinality-based clique potentials. 

Jan-15 (afternoon) 

Richard Zemel

Tutorial: Learning to label complex images

Research: A Flexible Framework for Incorporating High-Order Constraints 

Jan-16 (morning) 

B. Ravindran

Tutorial: Reinforcement learning

Research: Selective attention and visual processing 

Jan-16 (afternoon) 

Jitendra Malik

Tutorial 2: Orientation templates, Support Vector Machines, and Object Detection 

Jan-17 (morning) Discussion of research opportunities  




Brendan Frey, University of Toronto
Manik Varma, Microsoft Research India


Local Organizing Committee

K. R. Ramakrishnan, Indian Institute of Science 
B. Ravindran, Indian Institute of Technology Madras 
Sunita Sarawagi, Indian Institute of Technology Bombay 


Support from CIFAR and MSR India

Dr. P. Anandan (Microsoft Research India, Managing Director) 
Michael Hunter (CIFAR, Research Officer)
Vidya Natampally (Microsoft Research India, Director Strategy) 
Dr. Sue Schenk (CIFAR, Director, Programs and Information Systems) 
Ashwani Sharma (Microsoft Research India, Manager - External Research)
Dr. Mel Silverman (CIFAR, Vice-President, Research) 



The organizers would like to acknowledge additional support provided by the Indian Institute of Science, Bangalore, and the Department of Electrical and Computer Engineering at the University of Toronto.