inf536 - Computational Intelligence II (Complete module description)

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 Engineering of Socio-Technical Systems (Master) > Systems Engineering Master's Programme Environmental Modelling (Master) > Mastermodule
Responsible persons	Kramer, Oliver (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites	useful previous knowledge: Linear Algebra, Stochastics
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 The Students: will learn Deep Learning expertise, which are essential qualifications as AI experts and Data Scientists. Methodological competence The Students: learn the methods mentioned as well as the implementation in Python, NymPy and Keras. Social competence The Students: are encouraged to discuss the taught content in groups and work together to implement the programming tasks in the exercises Self-competence The 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
Recommended reading	Deep Learning by Aaron C. Courville, Ian Goodfellow und Yoshua Bengio
Links
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	every summer term
Module capacity	unlimited
Teaching/Learning method	V+Ü

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	2	SuSe	28
Exercises	2	SuSe	28
Total module attendance time			56 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	lecture-free period at the end of the semester	written exam, e-exam