Neural Networks and Intelligent Systems

Code 3.4.3319.9
Semester 9th
Flow Y - Computer Systems
Category Obligatory by selection
Credits 4
Class Hours - Lab Hours 2 - 2
Lecturers Stefanos Kollias, Andreas-Georgios Stafylopatis, Giorgos Stamou, Georgios Alexandridis (T & R Associates), Georgios Siolas (T & R Associates), Paraskevi Tzouveli (T & R Associates)
Links MyCourses
Web Platform Class 1: Microsoft Teams


The course covers the area of ​​neural networks with reference to other techniques from the broader area of ​​computational intelligence. It explores neural network models and architectures, dynamic behavior, convergence and stability, learning algorithms, implementations, computational capabilities, and applications. Feed-forward networks and learning through error correction (multi-layer perceptron and backpropagation). Support vector machines (SVM). Associative networks, Hopfield networks, recurrent multilayer networks. Competitive learning and Kohonen maps. Combinatorial optimization algorithms. Genetic algorithms. Deep learning: convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, generative adversarial networks (GANs). Reinforcement learning: dynamic programming, value iteration, Q-learning, deep Q-learning. Fuzzy logic and knowledge engineering. It also comprises laboratories on supervised learning, unsupervised learning, deep learning, and reinforcement learning.