Machine Learning

(3 Credit Hours) · Undergraduate

This course on machine learning will give an overview of many concepts, learning theory, techniques, and algorithms in machine learning, such as in reinforcement learning, supervised learning, unsupervised and semi-supervised learning, genetic algorithms, including advanced methods such as sequential learning, active learning, support vector machines, graphical and relational models. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The course will also include discussions on some of the recent applications, and the interface with computer vision, systems, bioinformatics, and architecture. The course will have a strong focus on project and assignments, with emphasis on writing implementations of learning algorithms.