|
Chief Patron
Sanghamitra Bandyopadhyay
Program Chair
Swagatam Das
Program Coordinator
Partha Pratim Mohanta
Advisory Committee
Bhabatosh Chanda
Debapriyo Majumdar
Dipti Prasad Mukherjee
Naqueeb Ahmad Warsi
Pinakpani Pal
Sumana Ghosh
Utpal Garain
Organizing Chairs
Aditya Panda
Anish Chakrabarty
Faizanuddin Ansari
Kushal Bose
Communication Chairs
Susmita Ghosh
Indranil Ojha
Finance Chairs
Dipesh Chanda
Sekhar Sarkar
Turbasu Biswas
Speakers
Professors, Scientists, Post-docs and Research Scholars from ISI, other eminent institutions and R&D labs.
Organizing Committee
Anal Roy Chowdhury
Arkaprabha Basu
Priyobrata Mondal
Sourav Saha
Sreeya Ghosh
Suchismita Dey
Suman Ghosh
External Advisory Committee
Abhishek Kumar
Avisek Gupta
Bikash Santra
Sandip Paul
Sankha Subhra Mullick
Shounak Dutta
Web Chair
Dilip Kumar Gayen
Contacts
E-mail: ecsu.wsdl2023@gmail.com
Mobile:
(+91) 9674853651 (Priyobrata)
(+91) 7003615040 (Anal)
|
The Objective: The Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata is organizing the Winter School on Deep Learning: From Perceptrons to Transformers. This winter school will focus heavily on imparting hands-on experience towards developing solutions for real-world challenging problems, in addition to making the associated theory easy to understand. Participants will learn from the basics of machine learning to the advanced deep learning-based approaches with application to Computer Vision and Natural Language Processing. Theoretical lectures will be delivered by renowned professors and scientists (from ISI and other esteemed organizations) who have made significant contributions in their areas of research. The lectures will be supplemented by extremely detailed hands-on sessions instructed by post-docs and research scholars.
Course coverage: The winter school will have the following course structure (theory and associated hands-on)
-
Basics of Python
-
Basics of the Deep Learning Library: PyTorch
-
Essentials of Matrix Calculus and Linear Algebra for Machine Learning
-
Bird’s Eye View of Machine Learning
-
Primer on Text, Video and Image Data Processing
-
Gradients-based Optimization Techniques
-
Rudiments of Artificial Neural Networks and Backpropagation of Error
-
Step towards Deep Learning: Activation functions, Normalization techniques, Regularization methods and Loss functions
-
Convolutional Neural Networks (CNNs)
-
Recurrent Neural Networks and Backpropagation through time (BPTT)
-
Attention Models and Transformer (BERT and Visual Transformer)
-
Deep Generative Models (GAN and VAE)
-
Weakly Supervised Deep Learning, Self-Supervised Learning
-
Emerging Learning Strategies: Semi-supervised, Few-shot and Zero-shot
-
Deep Reinforcement Learning
-
Explainable Artificial Intelligence
-
Geometric Deep Learning
-
Diffusion-based Models
-
Classic Real-world Application (Medical Image Analysis, Image Segmentation, Test Classification, Class Imbalanced Learning, Sports Analytics)
Mode of tutorials: Lectures and Hands-on sessions will be conducted in online mode only. All sessions will be on Fridays and Saturdays, and the recordings will be shared with all the participants.
Who can apply? Professionals from academia and industry, research/project scholars, masters and final-year bachelors students. Interested candidates must submit the online application (https://sites.google.com/view/wsdl2023/apply). Selected applicants will be informed to register for the school.
|