This is an introductory course on Machine Learning (ML) that is offered to undergraduate and graduate students. The contents are designed to cover both theoretical and practical aspects of several well-established ML techniques. The assignments will contain theory and programming questions that help strengthen the theoretical foundations as well as learn how to engineer ML solutions to work on simulated and publicly available real datasets. The project(s) will require students to develop a complete Machine Learning solution requiring preprocessing, design of the classifier/regressor, training and validation, testing and evaluation with quantitative performance comparisons.
1. Students are able to explain the different types of learning problems along with some techniques to solve them.
2. Students are able to model real-world problems, apply different learning techniques and quantitatively evaluate the performance.
3. Students are able to Identify and use advanced techniques through existing machine learning tools and libraries.
4. Students are able to analyze performance of ML techniques and comment on their limitations.