1. Rabczuk, T., & Bathe, K.-J. (2023). Machine Learning in Modeling and Simulation: Methods and Applications. Springer Cham.
2. Brunton, S. L., & Kutz, J. N. (2022). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (2nd ed.). Cambridge University Press.
3. Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into Deep Learning. Cambridge University Press.
4. Douglas C. Montgomery, Design and Analysis of Experiments (2012), John Wiley and Sons, Inc

This course is aimed at participants interested in learning to use tools from data analysis, machine learning, and artificial intelligence for solving real world problems in science and engineering. The emphasis is on identifying and modelling problems, collecting and curating data, building models and interpreting the results, in various domains.

Bachelor of Engineering or Science.

Pre-requisites

Inclination to learn mathematical aspects of ML and AI; Python programming

Corporate employees willing to up-skill or re-skill, Fresh graduates, Government employees working on big data analysis.

Introduction to data driven problem solving; Data types, collection and curation; Introduction and relevance of Data Analysis (DA), exploratory DA, visualization. Foundational statistics; Introduction to machine learning; types and models of learning. Neural networks and deep learning; Modern AI systems; Real-world case studies.