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Seminar Medical Applications and Deep Learning (SemMADL)- Lecturers
- Prof. Dr.-Ing. habil. Andreas Maier, Dr. rer. biol. hum. Ludwig Ritschl, Prof. Dr.-Ing. Joachim Hornegger
- Details
- Seminar
4 cred.h, ECTS studies, ECTS credits: 5
nur Fachstudium, Sprache Englisch
Time and place: Mon 8:15 - 9:45, KH 1.021
- Fields of study
- WPF INF-MA ab 1
WPF MT-MA-BDV ab 1
WPF CE-MA-TA-MT ab 1
- Contents
- Artificial neural networks and the area of deep learning (https://en.wikipedia.org/wiki/Deep_learning) have recently started to attract significant interest in the areas of machine learning and image processing. Different types of artificial neural networks exist, such as deep neural networks, convolutional neural networks and recurrent neural networks. Current research focuses on a wide range of topics, including artificial intelligence, speech and object recognition.
In this seminar, the basics for artificial neural networks will be
investigated with an additional focus on medical applications.
Additionally, students will have the chance to train their own
networks.
- Recommended literature
- Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, Arxiv, 2012.
Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press Gradient-Based Learning Applied to Document Recognition, Yann Lecun, 1998 Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Srivastava et al. 2014 Greedy layer-wise training of deep networks, Bengio, Yoshua, et al. Advances in neural information processing systems 19 (2007): 153. Reducing the dimensionality of data with neural networks, Hinton et al. Science 313.5786 (2006): 504-507. Training Deep and Recurrent Neural Networks with Hessian-Free Optimization, James Martens and Ilya Sutskever, Neural Networks: Tricks of the Trade, 2012. Deep boltzmann machines, Hinton et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Pascal Vincent et al. A fast learning algorithm for deep belief nets, Hinton et al., 2006 ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012 Regularization of Neural Networks using DropConnect, Wan et al., ICML OverFeat: Integrated recognition, localization and detection using convolutional networks. Computing Research Repository, abs/1312.6229, 2013. http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial http://deeplearning.net/tutorial/ Deep Learning Course on Coursera by Hinton DL platform with GPU support: caffe, lasagne, torch etc.
- ECTS information:
- Credits: 5
- Additional information
- Keywords: deep learning; machine learning; medical applications
Expected participants: 15, Maximale Teilnehmerzahl: 15
www: https://www5.cs.fau.de/lectures/ss-17/seminar-medical-applications-and-deep-learning-semmadl/
Registration is required for this lecture.
Die Registration via: persönlich beim Dozenten
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2017:
- Seminar Medical Applications and Deep Learning (SemMADL)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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