INFO621

This course explores advanced Machine Learning concepts and theories for learners who have already developed a fundamental understanding of basic methods for pattern recognition and have interest in applied work across contexts and disciplines. This course will advance students' knowledge of machine learning algorithms, neural networks, and a range of deep learning tools, as well as advanced clustering applications and related topics. Students will read and discuss contemporary research from top-tier machine learning conferences and will engage in advanced projects that rely on data to improve system performance.

Students are required to have taken an introductory course in machine learning. Students should have background knowledge in probability theory, linear algebra, optimization methods, and strong programming skills, preferably in Python or R.