Basics of Linear Algebra, Engineering Mathematics
Course Contents
- Introduction to the course
- Review of probability theory
- Introduction to uncertainty quantification (UQ)
- Monte Carlo simulation (MCS) for UQ
- Theory of Bayesian inference
- Supervised machine learning: Polynomial chaos expansion
- Supervised machine learning: Gaussian process
- Unsupervised learning: Clustering
- Unsupervised learning: Model order reduction
- Neural networks and deep neural networks
What you'll learn
Students will learn about the basic and advanced machine learning approaches along with applications for a variety of engineering problems.
About the Instructor
Dr. Biswarup Bhattacharyya is a faculty of the Stochastic Computational Modeling (SCM) Lab in the Department of Civil Engineering at IIT Hyderabad. His research areas are uncertainty quantification, physics-informed machine learning for engineering problems, digital twin, etc. He has extensive research experience from France, Switzerland and the US. The proposed course is the outcome of his research work, and it will be particularly interesting for engineering students looking to delve into machine learning.
Instructor ProfileCourse Assessment
Assessment may consist of assignments and/or quizzes and/or viva and/or exams.