August 1 -10, 2016
A probabilistic model (also sometimes referred as graphical model), provides a statistical analysis tool in which a graph expresses the conditional dependence structure between random variables. It has the ability to estimate the probability of an event occurring again on the basis of past data. Belief propagation deals with performing inference on probabilistic models, such as Bayesian networks and Markov random fields. Probabilistic models and Belief propagation are commonly used in artificial intelligence, machine learning and information theory. It has been successfully applied in the development numerous applications in the field on computer vision, artificial intelligence, machine learning, statistical physics and coding theory.Click here for the course brochure.
Objective of this course is to understand and learn how to use probabilistic models and belief propagation in creating artificial intelligence systems. It aims at providing the advance knowledge on probabilistic models and belief propagation. The courses will be useful for the people working in the research areas such as image analysis, computer vision applications, biometrics, target recognition, space applications, speech processing, neural computing, forensics, bioinformatics and coding theory.
Schedule of the Course
Schedule of the Course : August 1-10, 2016 Total Number of days/ lectures : 10 days/ 30 lectures
Early Registration (on or before June 10,2016) Late registration (on or before July 10, 2016) After July 10, 2016 or onsite Participant from outside India USD 500 USD 600 USD 650 Participant from industry/Business organization Rs. 20,000 Rs. 24,000 Rs. 26,000 Participant from Academic Institution Rs. 5,000 Rs. 6,000 Rs. 6,500
The fee includes all instructional materials, computer use for tutorials, and lunch. The participants will be provided with single bedded accommodation on payment basis.
How to Apply?
To apply for the course, please follow the steps given below in order:
Step 1: Payment of Registration Fee: Payment for the registration fee can be made through online/offline mode. Online payment can be made through NEFT transfer and offline payment can be made through Demand Draft. Details regarding payment are as follows:
(i) By Demand Draft: Demand Draft should be drawn in favor of “Registrar, IIT Indore”, payable at Indore. Demand Draft should be drawn from SBI, ICICI, HDFC, Axis Bank, Bank of India, Corporation Bank, Canara Bank or IDBI bank only.
(ii) By NEFT Transfer: Transfer the amount to the account number given below:
Beneficiary Account No.: 1476101027440
Name of the Beneficiary: IIT Indore Project and Consultancy A/C
Name of Bank: Canara Bank
Branch: Indore Navlakha
IFSC Code: CNRB0001476
MICR Code: 42015003
Step 2: Registration: After completing the payment of registration fee, fill the application form available http://gian.iiti.ac.in/register.php to complete the registration.
If payment is made through Demand Draft, send your Demand Draft to the following address (also e-mail the scanned copy of the Demand Draft to email@example.com):
Dr. Surya Prakash
Discipline of Computer Science and Engineering,
Indian Institute of Technology Indore,
Simrol Campus, Khandwa Road,
Indore - 453552, India.
Registration can be also be done offline by filling the form printed in this brochure and sending it along with Demand Draft (print of the online payment receipt if payment is made online) to above mentioned address.
- Review of Basics: Basic probability concepts, probability spaces and events, independence and conditional independence, Bayes rule, discrete and continuous probability distributions,joint, conditional, and marginal distributions.
- Probabilistic Models: IID models, Mixture models, Markov Chains and Processes. Hidden Markov Models (HMMs), Stochastic Context
- Inference and Parameter Estimation: Clustering using mixture models, Expectation Maximization (EM), Viterbi and Forward-Backward recursions for Hidden Markov Models, Inside-Out and Cocke–Younger–Kasami (CYK) algorithms for Stochastic Context Free Grammars.
- Dynamic Programming and Belief Propagation: Dynamic programming and Belief Propagation as generalized abstractions for common algorithms, Implementation issues: scaling and computational scheduling options.
- Applications and Approximation: Decoding of Convolutional, Low-Density Parity-Check (LDPC), and Turbo Codes as an instance of Belief Propagation, Natural language processing and biomolecular sequence and structure modeling using HMMs and SCFG
Professor Gaurav Sharma is with the University of Rochester, where he is a Professor in the Department of Electrical and Computer Engineering, Department of Computer Science, Department of Biostatistics and Computational Biology, and Department of Oncology. From 2008-2010, he served as the Director for the Center for Emerging and Innovative Sciences (CEIS), a New York state supported center for promoting joint university-industry research and technology development, which is housed at the University of Rochester. From 1996 through 2003, he was with Xerox Research and Technology in Webster, NY first as a member of research and technology staff and then as a Principal Scientist and Project Leader. He received the Ph.D. in Electrical and Computer Engineering from North Carolina State University, Raleigh, NC, and masters degrees in Applied Mathematics from NCSU and in Electrical Communication Engineering from the Indian Institute of Science, Bangalore, India. He received his bachelor of engineering degree in Electronics and Communication Engineering from Indian Institute of Technology, Roorkee (formerly, Univ. of Roorkee). Professor Sharma is a fellow of the IEEE, a fellow of SPIE, and a fellow of the Society for Imaging Science and Technology (IS&T). For more information visit: http://www.ece.rochester.edu/~gsharma/
Dr. Surya Prakash is currently an Assistant Professor in Discipline of Computer Science and Engineering at Indian Institute of Technology Indore, India. He received his MS and PhD degrees in computer science and engineering from Indian Institute of Technology Madras, India and Indian Institute of Technology Kanpur, India respectively. His research interest includes image processing, computer vision, pattern recognition, biometrics, and identity and infrastructure management. He has published several research articles in peer-reviewed international journals and conferences. He has also co-authored two books titled “IT Infrastructure and Its Management” published by Tata McGraw-Hill, India and “Ear Biometrics in 2D and 3D: Localization and Recognition” published by Springer. He has also been in the program committees of several international conferences in the field of pattern recognition, image processing and intelligent computing. For more information please visit : http://iiti.ac.in/people/~surya/
Who should attend this course?
- Research scholars, graduate students, researchers from different organization across the country working in the field of machine learning, computer vision, image analysis and coding theory.
- Young researchers working in R & D laboratories related to machine learning, computer vision, image analysis, and coding theory across the country.
- Faculty members and academicians interested in research in the field of machine learning, computer vision, image analysis, pattern recognition and coding theory
For any further information and registration, please contact:
Dr. Surya Prakash
Research Group on Computer Vision, Patter Recognition and Biometrics,
Discipline of Computer Science and Engineering,
Indian Institute of Technology Indore, Indore-453552, India.
E-mail: firstname.lastname@example.org, email@example.com