Course Name: Machine Learning for Physical Sciences

Faculty Name: Dr. Sangkha Borah

Course Period: 27 ^th Jul to 13 ^th Nov 2026 (Every Monday and Thursday from 17:30 - 18:55 (1.5 hours) IST )

Number of Credits: 3 credits

Prerequisite: Basic knowledge of mathematics (mainly probability theory) and Python programming (although a crash course would be given)

Course Contents:

• Introduction to Machine Learning
• A crash course on python
• Supervised Learning
• Unsupervised Learning
• Neural Networks and Deep Learning
• Reinforcement Learning
• Other Machine Learning Methods
• Applications in Physical Sciences

What you'll learn: This course provides students with a comprehensive understanding of, and hands-on experience with, machine learning algorithms ranging from basic to advanced levels, especially focussing on applications in physical sciences. The course is justified by the transformative shift currently occurring across the scientific landscape, where the traditional paradigms of theory and experiment are being augmented by data-driven discovery. As modern experiments in physics, chemistry, and materials science generate increasingly massive and complex datasets, conventional analytical methods often reach their limits. This course bridges the gap between fundamental physical principles and advanced computational intelligence, equipping researchers with the tools to automate pattern recognition, accelerate simulations, and uncover hidden correlations in high-dimensional data. By integrating machine learning into the physical sciences, students can tackle previously intractable problems such as predicting molecular properties, optimizing quantum circuits, or discovering novel functional materials, thereby driving innovation at the intersection of AI and fundamental science. By the end of this course, the students will be able to:

• 1. Master Core ML Architectures: Develop a deep understanding of supervised and unsupervised learning, ranging from classical regression and clustering to advanced neural network architectures like CNNs, RNNs, and Transformers.
• 2. Apply Physics-Informed AI: Learn how to incorporate physical constraints (such as conservation laws or symmetries) into machine learning models to ensure they remain physically consistent and interpretable.
• Supervised Learning
• Unsupervised Learning
• Neural Networks and Deep Learning
• Reinforcement Learning
• Other Machine Learning Methods
• Applications in Physical Sciences

Fee: Rs. 15,000/- Plus GST

Payment Link: Click here

Apply here: Apply Here

Last Date for Registration and Payment: 17th July 2026