ST228: Data Analysis, Machine Learning, and Artificial Intelligence

Term: January

Credits: 3:0  

For graduate students  

This course is aimed at graduate students working in data analysis, machine learning, and artificial

intelligence. As outlined in the course content, it exposes the student to collect and analyze data. It also

bridges the gap of physics and mathematics giving due importance to both. Students will learn essential

concepts, techniques, and tools for processing, analyzing, and making predictions from data, as well as

leveraging AI technologies for problem-solving and decision-making. Through hands-on projects and real

world examples, participants will gain practical skills to apply data analysis and machine learning

techniques in various domains

Prerequisites: None 

Syllabus  

Introduction to data analysis and tools: Introduction to DA, importance of DA, data types, collection and storage, data cleaning and preprocessing, exploratory DA, advanced exploratory DA, and feature engineering.

Machine learning basics: Introduction to ML, supervised learning, unsupervised learning, advanced.

Deep learning and AI applications: Introduction to deep learning and DI application, ANNs, CNNs, RNNs,

Natural language processing, Computer vision, Real-world case studies