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.

2023S, VU, 2.0h, 3.0EC
TUWEL

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • 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
* 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
Tue15:00 - 17:0007.03.2023 - 27.06.2023EI 1 Petritsch HS Lecture
Tue15:00 - 17:0014.03.2023HS 14A Günther Feuerstein Lecture
Tue15:00 - 17:0021.03.2023HS 14A Günther Feuerstein Lecture
Wed16:00 - 18:0019.04.2023EI 1 Petritsch HS Lecture
Wed16:00 - 18:0026.04.2023EI 1 Petritsch HS Lecture
Tue15:00 - 17:0002.05.2023HS 14A Günther Feuerstein Lecture
Tue15:00 - 17:0016.05.2023EI 1 Petritsch HS Lecture
Deep Learning for Visual Computing - Single appointments
DayDateTimeLocationDescription
Tue07.03.202315:00 - 17:00EI 1 Petritsch HS Lecture
Tue14.03.202315:00 - 17:00HS 14A Günther Feuerstein Lecture
Tue21.03.202315:00 - 17:00HS 14A Günther Feuerstein Lecture
Tue28.03.202315:00 - 17:00EI 1 Petritsch HS Lecture
Wed19.04.202316:00 - 18:00EI 1 Petritsch HS Lecture
Wed26.04.202316:00 - 18:00EI 1 Petritsch HS Lecture
Tue02.05.202315:00 - 17:00HS 14A Günther Feuerstein Lecture
Tue09.05.202315:00 - 17:00EI 1 Petritsch HS Lecture
Tue16.05.202315:00 - 17:00EI 1 Petritsch HS Lecture
Tue23.05.202315:00 - 17:00EI 1 Petritsch HS Lecture
Tue06.06.202315:00 - 17:00EI 1 Petritsch HS Lecture
Tue13.06.202315:00 - 17:00EI 1 Petritsch HS Lecture
Tue20.06.202315:00 - 17:00EI 1 Petritsch HS Lecture
Tue27.06.202315:00 - 17:00EI 1 Petritsch HS Lecture

Examination modalities

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

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Mon12:00 - 14:0004.11.2024EI 1 Petritsch HS written01.10.2024 08:00 - 01.11.2024 22:00TISSExam
Wed15:00 - 17:0022.01.2025EI 1 Petritsch HS written23.12.2024 08:00 - 20.01.2025 23:00TISSExam
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
17.02.2023 14:00 08.03.2023 23:00 13.03.2023 23:00

Precondition

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

Group Registration

GroupRegistration FromTo
Group 108.03.2023 13:0020.03.2023 23:00
Group 208.03.2023 13:0020.03.2023 23:00
Group 308.03.2023 13:0020.03.2023 23:00
Group 408.03.2023 13:0020.03.2023 23:00
Group 508.03.2023 13:0020.03.2023 23:00
Group 608.03.2023 13:0020.03.2023 23:00
Group 708.03.2023 13:0020.03.2023 23:00
Group 808.03.2023 13:0020.03.2023 23:00
Group 908.03.2023 13:0020.03.2023 23:00
Group 1008.03.2023 13:0020.03.2023 23:00
Group 1108.03.2023 13:0020.03.2023 23:00
Group 1208.03.2023 13:0020.03.2023 23:00
Group 1308.03.2023 13:0020.03.2023 23:00
Group 1408.03.2023 13:0020.03.2023 23:00
Group 1508.03.2023 13:0020.03.2023 23:00
Group 1608.03.2023 13:0020.03.2023 23:00
Group 1708.03.2023 13:0020.03.2023 23:00
Group 1808.03.2023 13:0020.03.2023 23:00
Group 1908.03.2023 13:0020.03.2023 23:00
Group 2008.03.2023 13:0020.03.2023 23:00
Group 2108.03.2023 13:0020.03.2023 23:00
Group 2208.03.2023 13:0020.03.2023 23:00
Group 2308.03.2023 13:0020.03.2023 23:00
Group 2408.03.2023 13:0020.03.2023 23:00
Group 2508.03.2023 13:0020.03.2023 23:00
Group 2608.03.2023 13:0020.03.2023 23:00
Group 2708.03.2023 13:0020.03.2023 23:00
Group 2818.03.2023 11:0020.03.2023 23:00
Group 2918.03.2023 11:0020.03.2023 23:00
Group 3018.03.2023 11:0020.03.2023 23:00
Group 3118.03.2023 11:0020.03.2023 23:00
Group 3218.03.2023 11:0020.03.2023 23:00

Curricula

Study CodeObligationSemesterPrecon.Info
066 645 Data Science Mandatory elective
066 926 Business Informatics Mandatory elective
066 932 Visual Computing Mandatory elective

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