Instructor: Jia Yuan Yu.
Lectures: Fridays, 14:00 - 17:00, in room C165 of Henry Grattan Building.
Course description: The course covers three subjects: supervised classification, sequential decision-making, and Markov decision problems. These topics span models of the environment that are stochastic, adversarial, and Markovian. We use both batch and online learning methods. Students will apply these methods on publicly available data sets using open-source software packages.
Course material: The lecture notes contain everything covered in class, and pointers to book sections for additional reading.
Course project: Twenty-five percent of the final grade is determined by a project. Students can pick to do an applied project using publicly available data sets, or a survey project on a topic in machine learning not covered in class. In either case, a report must be submitted before the end of the semester and a short presentation given in class.