Moodle: Most communication for this class will take place on Moodle. Please sign up here .
Lecture: Wednesdays, 13:30 - 16:00 o'clock, ZOOM for now, S103/226 when again possible
Exercises: Wednesdays, 16:15 - 17:00 o'clock, ZOOM for now, S103/226 when again possible
Lectures and exercises take place via Zoom. Please visit the Moodle page to find the link.
Motivation Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about statistical machine learning techniques, and gain some practice implementing them and getting them to work for yourself.
This course consists of a mix of online lectures and flipped classroom sessions. That is, aside from our online lectures, you may also want to watch the aditional videos provided, even before the lecture. Please prepare a list of 5 questions as extra homework for those videos, since we (all of you) may also try to answer your questions in class. We will also present our own work. We may even ask you to read papers, let's see. Aside from the “5-questions” homework, there are also four written assignments involving both theory and programming. Handing in those assignments regularly will earn you a bonus for the final exam.
This is the syllabus for the Summer 2020 iteration of the course.
Assignments and Exercises
Aside from the “5-questions” homework, there are also four written assignments involving both theory and programming. Handing in those assignments regularly will earn you a bonus for the final exam. Pleses visit Moodle for further information, and for submitting them.
Further Material and Links
- Online Mathematics for Ml book at https://mml-book.github.io/
- Model-based Machine Learning by John Winn an others