Learning outcomes 
Students understand the theory of neural networks, NatureInspired Computing (NIC) methods, and deep learning algorithms. Students get familiar with the most popular neural networks models (Perceptron, Adaline, multilayer neural networks, SelfOrganizing Map (SOM), etc.) and their learning algorithms (WidrowHoff rule, BackPropagation (BP) training, Backpropagation Through Time (BTT) training, competitive learning, etc.). Students master the essential knowledge of deep learning techniques, such as regularization, momentum, batch normalization, and dropout. Students understand the principles, structures, and algorithms of typical deep learning neural networks, e.g., Convolutional Neural Networks (CNN). Students know how to build up deep learning algorithms from scratch. Students gain the handon experiences in using deep learning techniques to deal with practical problems 
Content 
Elementary concepts and challenges of machine learning. Neural networks models (linear neural networks, feedforward neural networks, recurrent neural networks, SelfOrganizing Map (SOM), etc.). Deep reinforcement learning. Concepts and challenges of deep learning. Deep learning models and techniques (deep neural networks, Convolutional Neural Networks (CNN), AutoEncoder, Long Short Term Memory (LSTM), etc.). Applications of deep learning in classification, prediction, pattern recognition, etc. 
Modes of study 
Lectures, teaching materials, exercises, and examination 
Teaching methods 
distance teaching and study 
Study materials 
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2017, A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley & Sons Ltd, 2005, S. Haykin, Neural Networks, A Comprehensive Foundation, Prentice Hall, 2008, lecture slides and notes, selected papers from journals and conference proceedings 
Evaluation criteria 
Grading (80% examination and 20% computer exercises): 05 
Prerequisites 
Basic knowledge of linear algebra and probability theory 
Time 
Fall semester 
Offering data 
This course is open to everyone 
Further information 
Teaching language: English 
