183.605 Machine Learning for Visual Computing
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2018W, VU, 3.0h, 4.5EC
TUWEL

Properties

  • Semester hours: 3.0
  • Credits: 4.5
  • Type: VU Lecture and Exercise

Aim of course

Understanding of principles of machine learning. Application of machine learning methods to visual computing.

Subject of course

- linear models for regression and classification (Perceptron, Linear Basis Function Models, RBF, historical overview), applications in computer vision

- neural nets

- error functions and optimization (e.g., pseudo-inverse, gradient descent, newton method)

- model complexity, regularization, model selection, VC dimension 

- kernel methods: duality, sparsity, Support Vector Machine

- principal component analysis and Hebbian rule, canonical correlation analysis

- bayesian view of the above models, bayesian regression, relevance vector machine

- clustering und vektor quantisierung (e.g., k-means)

- Overview of deep learning models 

 

Additional information

ECTS Breakdown:

4.5 ECTS = 112.5 hours
30     lecture time
70     2 assignments (including studying machine learning principles, 
       reading documents and literature, 
implementation of learning algorithms in MATLAB or in a similar framework and writing documentation)
2.5    2 interviews (including preparation time)
10     written exam incl. preparation time    

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Tue12:00 - 14:0002.10.2018 - 22.01.2019EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue12:00 - 14:0002.10.2018EI 8 Pötzl HS - QUER Presentation and first lecture
Machine Learning for Visual Computing - Single appointments
DayDateTimeLocationDescription
Tue02.10.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue02.10.201812:00 - 14:00EI 8 Pötzl HS - QUER Presentation and first lecture
Tue09.10.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue16.10.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue23.10.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue30.10.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue06.11.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue13.11.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue20.11.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue27.11.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue04.12.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue11.12.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue18.12.201812:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue08.01.201912:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue15.01.201912:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605
Tue22.01.201912:00 - 14:00EI 8 Pötzl HS - QUER Lecture MLVC 183.605

Examination modalities

  • Two assignments with interviews.
  • One written exam. 

Course registration

Begin End Deregistration end
24.09.2018 09:00 10.10.2018 23:59 10.10.2018 23:59

Registration modalities

Please register for the course in TISS. After registration you can team up as a group of 3 students in TUWEL.

Curricula

Literature

No lecture notes are available.

Previous knowledge

Knowledge of linear algebra and probability theory

Continuative courses

Miscellaneous

Language

German