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183.663 Deep 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.

2024S, VU, 2.0h, 3.0EC
TUWELLectureTube

Course evaluation

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • LectureTube course
  • Format: Presence

Learning outcomes

After successful completion of the course, students are able to develop and apply deep learning methods for automatic image analysis (e.g. for classification of images or detection of people in images). 

 

Subject of course

Deep learning for automatic image analysis:

* Brief recap of Computer Vision and Image Processing
* Machine Learning: overview, parametric models, iterative optimization
* Feedforward Neural Networks, backpropagation
* Convolutional Neural Networks for classification, detection, and segmentation
* Generative models for image synthesis
* Deep Learning for 3D and unstructured data
* Software libraries and practical aspects
* Preprocessing, data augmentation, regularization, visualizations
* Algorithmic Governance, Trustworthy AI and ethical Aspects

The contents presented in the lecture will be applied in exercises.

 

Teaching methods

Lecture and individual programming tasks in groups of two.

Mode of examination

Written

Additional information

ECTS breakdown: 3 ECTS = 75h

16h lecture
34h programming exercises
24h exam preparations
1h exam
---
75h

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Tue11:00 - 13:0005.03.2024 - 25.06.2024FAV Hörsaal 1 Helmut Veith - INF Lecture
Deep Learning for Visual Computing - Single appointments
DayDateTimeLocationDescription
Tue05.03.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue12.03.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue19.03.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue09.04.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue16.04.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue23.04.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue30.04.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue07.05.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue14.05.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue28.05.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue04.06.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue11.06.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue18.06.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Tue25.06.202411:00 - 13:00FAV Hörsaal 1 Helmut Veith - INF Lecture

Examination modalities

Written exam (50%) and compulsory programming exercises (50%).

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Wed - 16.10.2024written16.09.2024 00:00 - 11.10.2024 23:55TISSDeep Learning for Visual Computing Exam 2
Wed - 11.12.2024written11.11.2024 00:00 - 06.12.2024 23:55TISSDeep Learning for Visual Computing Exam 3
Wed - 22.01.2025written22.12.2024 00:00 - 17.01.2025 23:55TISSDeep Learning for Visual Computing Exam 4
Wed11:00 - 13:0011.06.2025EI 7 Hörsaal - ETIT written11.05.2025 09:00 - 10.06.2025 09:00TISSDeep Learning for Visual Computing Exam 1

Course registration

Begin End Deregistration end
16.02.2024 14:00 13.03.2024 23:00 15.03.2024 23:00

Precondition

The student has to be enrolled for at least one of the studies listed below

Curricula

Study CodeObligationSemesterPrecon.Info
066 645 Data Science Mandatory elective
066 926 Business Informatics Mandatory elective
066 932 Visual Computing Mandatory elective
860 GW Optional Courses - Technical Mathematics Not specified

Literature

  • Deep Learning, Goodfellow et al., MIT Press, 2016
  • The Science of Deep Learning, I. Drori, Cambridge University Press, ISBN: 9781108835084

Preceding courses

Miscellaneous

Language

English