Stud.IP Uni Oldenburg
University of Oldenburg
06.10.2022 23:26:41
inf536 - Computational Intelligence II (Complete module description)
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Module label Computational Intelligence II
Modulkürzel inf536
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Computing Science
Verwendbarkeit des Moduls
  • Master Applied Economics and Data Science (Master) > Data Science
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik
  • Master's Programme Computing Science (Master) > Angewandte Informatik
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction
  • Master's Programme Environmental Modelling (Master) > Mastermodule
Zuständige Personen
Kramer, Oliver (Module responsibility)
Lehrenden, Die im Modul (Prüfungsberechtigt)
Skills to be acquired in this module

In the lecture "Convolutional Neural Networks" you will learn the basics of Convolutional Neural Networks, from methodological understanding to implementation.

**Professional competence**
Students will learn Deep Learning expertise, which are essential qualifications as AI experts and Data Scientists.

**Methodological competence**

Students learn the methods mentioned as well as the implementation in Python, NymPy and Keras.

** Social competence **
Students are encouraged to discuss the taught content in groups and work together to implement the programming tasks in the exercises.

**Self-competence **
Students are guided to conduct independent research on advanced methods as the teaching field changes dynamically.
Module contents
Students learn the basics of machine learning and in particular the topics of dense layers, cross-entropy, backpropagation, SGD, momentum, Adam, batch normalization, regularization, convolution, pooling, ResNet, DenseNet, and convolutional SOMs.

Deep Learning by Aaron C. Courville, Ian Goodfellow und Yoshua Bengio

Language of instruction English
Duration (semesters) 1 Semester
Module frequency once a year
Module capacity unlimited
Modullevel / module level AS (Akzentsetzung / Accentuation)
Modulart / typ of module je nach Studiengang Pflicht oder Wahlpflicht
Lehr-/Lernform / Teaching/Learning method V+Ü
Vorkenntnisse / Previous knowledge - inf535 Computational Intelligence I
- Statistik
Form of instruction Comment SWS Frequency Workload of compulsory attendance
Lecture 2 SoSe 28
Exercises 2 SoSe 28
Präsenzzeit Modul insgesamt 56 h
Examination Prüfungszeiten Type of examination
Final exam of module
lecture-free period at the end of the semester