This course will introduce a number of advanced concepts in the field of machine learning such as: Support Vector Machines, Gaussian Processes, Recommender Systems, Deep Networks, Reinforcement learning and Relational Learning techniques. It is assumed that the student has previously completed a basic machine learning course, as the focus of this course will be on more recent developments and state of the art in machine learning research. The goal of this course is to familiarize the student with powerful machine learning techniques, often with a statistical background, and to introduce them to non-standard techniques and representations that can be used for complex problems. The lectures in this course will be accompanied by labs and assignments, which will give the students the opportunity to implement or work with these techniques and will require them to read and understand published scientific papers from recent Machine Learning conferences. After completing this course the student will be able to understand, adapt and apply a number of advanced machine learning approaches. The student will be able to select the correct representation for a machine learning problem, and to translate such a problem into a suited representational format.
Desired Prior Knowledge: Familiarity with the basics of machine learning through a Machine Learning or Data Mining course
Recommended literature: Pattern Recognition and Machine Learning - C.M.Bishop; Bayesian Reasoning and Machine Learning - D. Barber; Gaussian Processes for Machine Learning - C.E. Rasmussen & C. Williams; The Elements of Statistical Learning - T. Hastie et al.