Learning outcomes 
Students understand the underlying principles of machine learning and data mining. 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, SOM, etc.) and their learning algorithms (WidrowHoff rule, BackPropagation (BP) training, Backpropagation Through Time (BTT) training, competitive learning, etc.). Students can apply learning algorithms for adjusting the weights in these neural networks. Students understand the basics of optimization theory and techniques. Students know the most widely applied swarm algorithms (PSO, ACO, DE, etc.). Students are able to use these swarm intelligence methods for solving optimization problems. Students understand the working principles of a few representative reinforcement learning methods (Adaptive Critic Design (ACD), Temporal Difference (TD), 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., CNN. Students know how to build up deep learning algorithms from scratch. Students know how to choose evaluation metrics, how to preprocess data sets, and how to handle variance and bias in deep learning. Students are able to program deep learning using the TensorFlow platform. 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.). Metaheuristics optimization methods (Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE), etc.). Reinforcement learning. Concepts and challenges of deep learning. Deep learning models and techniques (deep neural networks, Convolutional Neural Networks (CNN), etc.). Applications of deep learning in classification, prediction, pattern recognition, etc. 
Modes of study 
Lectures, teaching materials, exercises, and examination. 
Teaching methods 
Lectures 32 hours, exercises 16 hours. 
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: 05 
Prerequisites 
Basic knowledge of linear algebra and probability theory, general MATLAB programming skills 
