Machine Learning for Computer Vision
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
IN2357 is a semester module in English language at Master’s level which is offered every semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Content, Learning Outcome and Preconditions
- kernel methods, specifically Gaussian processes
- metric learning
- clustering such as GMMs or spectral clustering
- boosting and bagging
- hidden Markov models
- neural networks and deep learning*
- sampling methods, specifically MCMC
The focus here is laid on a broad understanding of these methods rather than in a deep specification of single approaches. Practical experience is acquired by means of programming tasks.
*The topic “deep learning” will be handled only marginally. For a broader treatment of this topic, we refer to other classes, e.g. IN2346.
Statistical modeling and machine learning (IN2332)
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||4||Machine Learning for Computer Vision (IN2357)||Triebel, R.||
Fri, 12:00–14:00, Interims I 102
Thu, 16:00–18:00, Interims I 102
Learning and Teaching Methods
Kevin Murphy: Machine Learning: A Probabilistic Perspective
Carl Edward Rasmussen and Christopher Williams: Gaussian
Processes for Machine Learning
Description of exams and course work
There is a possibility to take the exam in the following semester.