|Module label||Computational Intelligence II|
|Credit points||6.0 KP|
|Institute directory||Department of Computing Science|
|Applicability of the module||
Lehrenden, Die im Modul (Authorized examiners)
Kramer, Oliver (Authorized examiners)
|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.
Students will learn Deep Learning expertise, which are essential qualifications as AI experts and Data Scientists.
Students learn the methods mentioned as well as the implementation in Python, NymPy and Keras.
Students are encouraged to discuss the taught content in groups and work together to implement the programming tasks in the exercises.
Students are guided to conduct independent research on advanced methods as the teaching field changes dynamically.
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|
|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
|Course type||Comment||SWS||Frequency||Workload of compulsory attendance|
|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