Inhalt des Dokuments
Introduction to Machine Learning
Linear Regression, Logistic Regression, Regularization, Full-connected Neural Networks, Backpropagation algorithm, Convolutional Neural Networks, the Bayes Theorem, application to selected problems.
Machine learning is the science of getting computers to act without
being explicitly programmed. It rapidly evolving in the modern world
and is needed for many problems in engineering, business, medicine
etc. For example, in the past decade machine learning has given us
self-driving cars, practical speech recognition, the victory of a
computer over one of the world's strongest players of the game Go and
helped us in the problem of particle identification in LHC
This course addresses students majoring in computer science, engineering sciences, physics, mechanical engineering, and similar university courses. Basic knowledge of linear algebra and confident programming is helpful.
The course is given as a block seminar in June and July by Dr. Murachev in a four-week period. In the first lecture (Wednesday, 19.06., 14:00--16:00, room: MS 307), the procedure of the course and the exam regulations are detailed. There are two lectures and two consultation hours per week until Friday, 12.07. Please see the module descriptor for further information on the schedule.
See this link .