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.

2019W, VU, 3.0h, 4.5EC

Properties

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

Learning outcomes

After successful completion of the course, students are able to...

  • choose suitable methods for a given problem
  • employ suitable technologies, software-tools and standards for the solution of a given problem
  • understand principles of machine learning

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

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

- Overview of deep learning models 

 

Teaching methods

  • implementation of methods discussed in the lecture using textual instructions
  • carrying out experiments using the implemented methods
  • documentation including decription and interpretation of results
  • oral feedback during assignment interviews

Mode of examination

Immanent

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 - 14:0001.10.2019 - 21.01.2020EI 8 Pötzl HS Lecture MLVC 183.605
Tue12:00 - 14:0001.10.2019EI 8 Pötzl HS Presentation and first lecture
Machine Learning for Visual Computing - Single appointments
DayDateTimeLocationDescription
Tue01.10.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue01.10.201912:00 - 14:00EI 8 Pötzl HS Presentation and first lecture
Tue08.10.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue15.10.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue22.10.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue29.10.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue05.11.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue12.11.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue19.11.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue26.11.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue03.12.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue10.12.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue17.12.201912:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue07.01.202012:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue14.01.202012:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605
Tue21.01.202012:00 - 14:00EI 8 Pötzl HS Lecture MLVC 183.605

Examination modalities

  • two assignments
  • two assignment interviews
  • one written exam

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Tue12:00 - 13:3021.01.2020EI 8 Pötzl HS written06.01.2020 00:00 - 20.01.2020 23:59TISSVorlesungsprüfung
Mon15:00 - 16:3027.04.2020Seminarraum FAV 01 A (Seminarraum 183/2) written13.04.2020 00:00 - 26.04.2020 23:59TISSVorlesungsprüfung
Fri10:00 - 11:3012.06.2020 EI 9 Hlawka HSwritten27.05.2020 00:00 - 10.06.2020 23:59TISSVorlesungsprüfung

Course registration

Begin End Deregistration end
23.09.2019 09:00 09.10.2019 23:59 09.10.2019 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