CCE224: Machine Learning and AI for Real-World Science and Engineering

Term: January

Credits: 3:0  

For graduate students  

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.

Prerequisites: None 

Syllabus  

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.