Online Course on
AI Research Foundations
This intensive program is designed to equip educators and researchers with foundational principles and modern methodologies in Artificial Intelligence.
Know the Course Instructor
Dr Chiranjib Bhattacharyya
Dr. Chiranjib Bhattacharyya is a Professor in the Department of Computer Science and Automation (CSA) at the Indian Institute of Science (IISc), where he has been a faculty member since 2002 following a postdoctoral fellowship at UC Berkeley. He holds BE and ME degrees in Electrical Engineering from Jadavpur University and IISc, respectively, and earned his PhD from CSA, IISc. A widely cited researcher with numerous awards across top Machine Learning journals and conferences, his work focuses on the foundations of ML, optimization, and industrial applications. Dr. Bhattacharyya leverages his deep expertise in vector-space mathematics and optimization to guide through language model architectures, training dynamics, neural network fundamentals, and responsible ML deployment.
Objectives of the Course
The goal of this course is to empower faculty (particularly those in computer science and allied disciplines) to guide research projects in AI.
The course provides greater insights into strategies and challenges for guiding undergraduate students.
Course Syllabus
Language Model Fundamentals & ML Pipeline.
Core Concepts: Learn foundational language model concepts and the end-to-end ML development pipeline.
Architecture Comparisons: Evaluate strengths and trade-offs of traditional n-gram models versus advanced transformers.
Practical Coding Labs: Build hands-on insights into model mechanics to generate text and detect linguistic patterns.
Engineering Case Studies: Examine how research engineers operate and apply responsible ML to community problems.
Text Preparation & Ethical Data Design
Data Processing Techniques: Prepare and structure text using tokenization and embedding strategies.
Mathematical Representations: Work with vectors and matrices to understand how models encode semantic meaning.
Bias & Trade-Off Analysis: Evaluate critical data preparation decisions and mitigate potential dataset biases.
Ethical Dataset Design: Apply the Data Cards framework to ensure transparency, accountability, and community values.
Model Training & Societal Impact
Training Dynamics: Diagnose and resolve common model training issues, such as overfitting and underfitting.
Neural Network Labs: Implement and evaluate multilayer perceptrons (MLPs) to master backpropagation and classification mechanics.
Applied Case Studies: Review real-world case studies demonstrating neural networks in production.
Risk & Safety Assessment: Anticipate potential risks, safety concerns, and broader societal impacts of AI innovation.
Who Can Apply & Who Can Benefit?
Who Can Apply?
- Full-time faculty guiding undergraduate and postgraduate research project.
Who Can Benefit?
- This Faculty Development Program on AI Research Foundations is specifically tailored for academic faculty looking to bridge theoretical machine learning with modern generative AI deployment.
Ready to Enroll?
Apply online at iisc.online · New batches every semester