= Project Machine Learning (Projekt Maschinelles Lernen) = == General Information == ||'''First meeting'''||Wednesday, 24 October 2018, 10:15am, Room MAR 4.064 || ||'''Responsible'''|| [[http://www.ml.tu-berlin.de/menue/mitglieder/klaus-robert_mueller/|Prof. Dr. Klaus-Robert Müller]] || ||'''Contact'''|| Jacob Kauffmann j.kauffmann@tu-berlin.de || ||'''Language'''|| Englisch || ||'''Credit'''|| M.Sc. Modul (Projekt), 9 LP (ECTS), PÄS || ||'''Course Information'''|| [[https://isis.tu-berlin.de/course/view.php?id=14322]] || ||||'''''Enrollment closed: course is full'''''|| == First Meeting == Our first meeting is 24 Oct 2018 at 10:15am in Room MAR 4.064. == Enrollment / Limited number of participants == The course has a a limit of 10 participants. If you intend to participate, please send an e-mail to j.kauffmann@tu-berlin.de as early as possible since we assign spots mostly on a first come / first serve basis. It is mandatory to attend the first meeting or e-mail the course organizer if this should not be possible. Otherwise we assume that you have no interest in the course anymore and give your spot to someone else. === Prerequisites === There are no formal entry requirements. However, we strongly recommend * the lecture ''Machine Learning 1'', * the course ''Python Programming for Machine Learning'' and * ideally, the ''Lab Course Machine Learning''. Students who do not have attended these lecture, should make sure they have the following skills: * '''Basic theory:''' Students should be fluent in probability theory, linear algebra and understand how and why mainstream learning algorithms work. * '''Some practical ML experience:''' Prior exposure to the practical application of ML algorithms is essential; students should know how to select hyperparameters and assess the performance of a trained predictor. * '''Python programming:''' All code that is to be handed in must be written in Python; students should be able to program in Python using the packages `numpy` and `scipy`.