Winter School 2010 - Microsoft Research

Winter School 2010

THE 2010 WINTER SCHOOL ON
MACHINE LEARNING AND COMPUTER VISION

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

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

Speakers

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)

Description

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	Where computer vision needs help from computer science (and machine learning) Applications of belief propagation in low-level vision (part1, part2, part3, part4) Removing blur due to camera shake from images (part1, part2, part3)
Brendan Frey	Introduction to Machine Learning Probability and Maximum Likelihood Bayesian Learning & Estimation Theory Pattern Classification & Decision Theory Neural Networks and Kernel Methods Clustering Learning generative models of images
Yann LeCun	Deep Learning (part1, part2, part3) Learning Invariant Feature Hierarchies (part1, part2) Other Methods and Applications of Deep Learning
Jitendra Malik	Classification using intersection kernel SVMs is efficient How should we combine high level and low level knowledge? (part1, part2) Poselets: Body Part Detectors trained Using 3D Human Pose Annotations
Bruno Olshaussen	What we know and don't know about biological vision Natural image statistics and visual coding (part1, part2, part3)
B. Ravindran	Reinforcement Learning - Learning from Interaction Selective attention in Reinforcement Learning
Sunita Sarawagi	Graphical Models Structured learning Training algorithms for Structured Learning Querying for relations from the semi-structured Web
Manik Varma	Introduction to Machine Learning Multiple Kernel Learning
Martin Wainwright
Yair Weiss	Belief Propagation and Linear Programming - theory and applications Semi-supervised learning in huge image collections
Richard Zemel	Learning to label complex images A Flexible Framework for Incorporating High-Order Constraints

Schedule

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

Organization

Co-Chairs

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)

Acknowledgements

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.

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THE 2010 WINTER SCHOOL ONMACHINE LEARNING AND COMPUTER VISION