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University of Oldenburg
25.10.2021 09:11:40
inf536 - Computational Intelligence II (Complete module description)
Original version English Download as PDF
Module label Computational Intelligence II
Module code inf536
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Computing Science
Applicability of the module
  • 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
Responsible persons
Lehrenden, Die im Modul (Authorized examiners)
Kramer, Oliver (Authorized examiners)
Prerequisites
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.
Reader's advisory

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

Links
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
Course type Comment SWS Frequency Workload of compulsory attendance
Lecture
2 SuSe 28
Exercises
2 SuSe 28
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
lecture-free period at the end of the semester
Written