This lecture will introduce you to the basics of machine learning and data science methods that are at the core of many of today's AI systems. The lecture will be given jointly by professors Bastian Leibe, Wil van der Aalst, and Holger Hoos. The course covers the mathematical foundations of ML including probability basics, supervised and unsupervised learning, probability density estimation, linear discriminants and regression, logistic regression, Support Vector Machines, Kernels and Neural Networks. Finally, Experimental procedures in machine learning shall be introduced.
The lecture will be accompanied by small-group exercise sessions held by tutors. Those exercise sessions will prepare you for solving bi-weekly exercise sheets that you need to solve and submit electronically in order to get admitted to the exam. All exercises will take place in presence in different time slots. In addition, a bi-weekly plenary online exercise session will be offered, in which we will present the model solutions for the previous exercise sheet.
Registration to the lecture is handled via the RWTHonline system.
Prof. Dr. Holger H. Hoos
Chair Holder, Alexander von Humboldt Professor
E-mail: hh[at]aim[dot]rwth-aachen[dot]de
Phone: +49 241 80 21451
