Neural Networks and Deep Learning recently have gained strong interest
(Deep Learning has been considered one of 10 breakthrough technologies
by the MIT Technology Review 2013). The aim of the course is to provide a
fundamental understanding of important concepts, algorithms, techniques
and architectures of neural networks and deep learning.
After completing the course, students should
have a basic overview over neural network and deep learning
concepts, algorithms and architectures, suitable applications,
capabilities and limitations,
be able to apply suitable neural network and deep learning
techniques to new problems,
analyze the outcome of neural network and deep learning experiments
and explore potential methods to improve performance.
- Biological basis (neuron and networks)
- Artificial neuron models
- Artificial neural networks: Architectures and the learning problem
- Feedforward neural networks, multi-layer perceptron
- Learning in neural networks and the backpropagation algorithm
- Deep Learning: Motivation and concepts
- Convolutional neural networks
- (If time permits
Recurrent neural networks: Long Short Term Memory (LSTM)
- (If time permits
- (If time permits