SAiDL Season of Code 2022
SAiDL's Season of Code gives students to work on cutting-edge AI projects with a strong focus on Open Source. This year's program will run from June to December focusing on a broad range of topics through the following projects -
1. Meta-Learning with JAX
2. Event Vision Library
3. Exploring Deep Learning models for Visual Saliency Prediction
More information on the project goals, related topics, tools mentors and the sign-up form is available here.
[SAiDL & APPCAIR] Workshop on Probabilistic Graphical Models (Part I)
Dates: 18th April - 27th April, 2022
Session on Getting into Research
Speakers: Omatharv Vaidya, Vedant Shah, Sharad Chitlangia, Rajaswa Patil, Alish Dipani
[SAiDL & APPCAIR] AI Symposium 2021
Dates: 2nd & 3rd October 2021
Session Recordings: https://www.youtube.com/playlist?list=PLKwsK83XYnLVI9SmQ3zLzSdNyVoGrsGDF
CTE Course: Introduction to Causal Inference
Taught By: Sharad Chitlangia, Vedant Shah, Rishabh Patra, Soundarya Krishnan, Ishita Mediratta, Anmol Agarwal
CTE Course: Intro to Machine Learning and Deep Learning
Taught By: Vishwa Shah, Shrey Pandit, Hrithik Nambiar, Yash Bhartia, Sushmit Wani
SAiDL Season of Code 2021
SAiDL's Season of Code gives students to work on cutting edge AI projects with a strong focus on Open Source. This year's program will run from August to December focusing on a broad range of topics through the following projects -
1. Causal Inference in Time Series
2. Program Synthesis with Julia
3. Visualisation Library for Vision Transformers
4. Meta Learning with Jax
More information on the projects goals, related topics, tools and mentors is available here. Stay tuned for more details and the release of the sign-up form!
[SAiDL & APPCAIR] Summer Symposium on AI Research
Dates: 25th & 26th July 2020
Session Recordings: https://www.youtube.com/playlist?list=PLKwsK83XYnLUZR1PmKeSehCSK0M_Qg4pN
SAiDL Season of Code 2020
SAiDL's Season of Code gives students to work on cutting edge AI projects with a strong focus on Open Source.
List of Projects:
1. Open Source python package Adversarial NLP
2. Deep Contextual Bandits
3. Computer Vision for Sports Analytics
4. Knowledge Distillation library
5. Twitter Feed Distillation
6. Benchmarking Causal inference and Reinforcement learning algorithms on dynamic environments
7. Deep Learning package for Time Series Modelling
8. Exploring Applications of Spiking Neural Networks
More information on the project deliverables, description and mentors is available here.
QSTP: Introduction to Deep Learning
A Quark Summer Project on learning the fundamentals of deep learning and how they are applied in Computer Vision, Natural Language Processing and Reinforcement Learning.
CTE course: Introduction to Machine Learning
Taken by Rajaswa Patil and Pranav Mahajan
The course included introduction to fundamental concepts of Machine Learning and Deep learning and hand-on experience through projects and self-organized Kaggle competitions. Batch strength: 120 students.
CTE course: Advanced Computer Vision
TIP Projects and Courses
Learning to play games with RL, Computer Vision, Financial Market Modelling, Machine Learning and AI & Cognitive Neuroscience
TIP course: Advanced Deep Learning
Taken by Mehul Rastogi, Sharad Chitlangia, Rijul Ganguly and Ajay Subramanian. Slides and resources can be found here.
TIP course: Introduction to Deep Learning
Taken by Alish Dipani, Mehul Rastogi, Sharad Chitlangia, Rijul Ganguly. Slides and resources can be found here.