In SS 2024, the course will not be held. However, if you want to learn the material on your own or with recordings from previous years, there will be an exam.
Physics-Informed Machine Learning SS 2023
Quick Info
Time and Place
Offered every Summer Semester
Lecture: Thursdays 800-930
Exercise: Thursdays 1200-1245
Place: 5510.01.050 & online via Zoom.us (for meetingID & password check Moodle)
Program
MSc.
Language: English
5 ECTS, 2 VO + 1 UE
Literature
Bishop: Pattern Recognition and Machine Learning, Theodoridis: Machine Learning. Extra literature will be given in the lecture notes.
Exam
Type: written
Time & Place: 03.08.2023 at 1100 in 5510.02.001
Allowed: non-programable calculator
Exam Breakdown
The exam grade can be increased by 0.3 if (≥8) exercises are submitted and sufficient attempt was made to solve them.
What's this course about?
In this course, you will get to know some of the widely used machine learning techniques. We will cover methods for classification and regression, methods for clustering and dimensionality reduction, and generative models. In the exercise class, you will transform the theoretical knowledge into practical knowledge and learn how to use the machine learning tools to solve engineering problems. For a more detailed description see the module handbook.
How does it work?
LEARN THE BASICS
The theoretical background will be given in 2h/week lectures with a mixture of slides (motivational examples, key concepts), blackboard (important mathematical background), and animations (algorithm demonstrations).
Apply to Problems
Exercise handouts are given beforehand. During exercise class TAs give you hints on how to solve them and explain any questions about previous exercise. Computational problems can be solved in any programing language, however, the solutions will be in Python.
Download
All course material (lecture notes, slides, exercise handouts etc.) will be uploaded to Moodle.
Questions
For any organizational questions write an email to the head TA.
Online Support
Need help with exercises? You didn't quite understand the lectures? Post on Moodle and get fast feedback.
Learn from others
A collection of questions on Moodle allow you to learn from and help your fellow students.
Meet your teachers
Lecturer
