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. [helios](https://helios.ntua.gr/course/view.php?id=953)