This is an old revision of the document!

Lecture: Convex Optimization and Machine Learning

Mathematical Methods of Image and Pattern Analysis

Lectures: Prof. Christoph Schnörr
Exercises: Daniel and Jonathan
Format:
- Extensive lecture notes that you read on your own.
- Short videos that explain each subtopic from a top-down viewpoint. This is not a replacement for reading the lecture notes.
- Exercise sheets you work on your own and compare with our solution videos
- Mulitple Choice tests
- A feedback forum in Moodle where students can give feedback and ask questions.
- No meeting in HeiConf, etc.
- No in-person lecture
Language: English
SWS: 4
ECTS: 6
Lecture Id: MM35, Spezialisierungsmodul Numerik und Optimierung
Supplementary Practical: For doing Programming Exercises in May, June and July you get two extra credits (this might depend on your field of study.)
Registration: Please register in Müsli and Moodle (you do not need an enrollment key).
Feedback: We have a feedback forum in Moodle for all questions related to the mathematical content and the organization of the course.
Prior Knowledge: Required: Lineare Algebra and Analysis, Recommended: Convex and Nonlinear Optimization
Content: Discrete and Continuous Fourier Transformations, FIR Filter, Reproducing Kernel Hilbert Spaces, Markov Random Fields, Gaussian Markov Random Fields, Exponential Family, Elementary Differential Geometry