Advanced Topics in Machine Learning (Seminar) (2S, 4.0 LP)
|SWS||Type||Course Form||CP (Effort)||Presence-Time / Self-Study|
|-||K||4.0 CP||92 h|
|(2S)||4.0 CP||28 h||92 h|
|CP, Effort||4.0 CP = 120 h|
|Position of the semester||1 Sem. in WiSe/SuSe|
|Level|| Master (Advanced)|
|Area of study||[INF-KI] Intelligent Systems|
Possible Study achievement
- Verification of study performance: presentation
- Examination number (Study achievement): 67573 ("Deep Learning (Seminar)")
- Details of the examination (type, duration, criteria) will be announced at the beginning of the course.
Topics are flexible and according to student interests. They may include:
- Overfitting, regularization, and early stopping
- Stochastic Optimization
- Generative approaches (e.g., GANs, Universum Learning)
- Deep Bayesian learning
- Sophisticated architectures
- Piecewise-linear deep networks
- Globally optimal training of polynomial networks
- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning . MIT Press, 2016.
- Schmidhuber, Jürgen. "Deep learning in neural networks: An overview." Neural networks 61 (2015): 85-117.
- Selected research articles