In the seminar, the theoretical foundations of machine learning will be discussed. Topic include probably almost correct learning, VC dimension, risk minimization, boosting, model selection, stochastic gradient descent, support vector machines, kernel methods, and neural networks. After an introduction to the general topic of machine learning, students will present a chapter in the book “Understanding machine learning” by Shalev-Shwartz and Ben-David (Cambridge Universit Press) and hand in a short summary of the key findings. Participation in the discussions is expected.
Ungraded part of the Seminar: Presentation and discussion
No participant restriction (MA + PhD)
StO/PO MA 2016: 6 LP, Modul: "Selected Topics in Quantitative Methods"
StO/PO MEMS 2016: 6 LP, Modul: "Selected Topics in Quantitative Methods", Major: Quantitative Methods