PART A
Elements of theory of computation. Computational Intelligence. Machine Learning. Neural networks, fuzzy logic and evolutionary computation. Natural computing and computational intelligence. Elements of optimization for computational intelligence. Theoretical foundations and problems. No-free lunch theorem. Different aspects of optimization (combinatorial, global, local, constrained, etc.). Multi-objective optimization, problems and applications. Evolutionary computation and algorithms. Genetic algorithm. Basic principles and mechanisms (selection, crossover and mutation). Techniques of evolution. Genetic programming, grammatical evolution and evolutionary strategies. Different versions of genetic and evolutionary algorithms. Applications. Algorithms based on the social behavior of populations. Swarm intelligence. Particle swarm optimization. Basic approach and different versions. Issues related to initialization, convergence and exploration of the space of feasible solutions. Exploration and exploitation. Applications of particle swarm optimization. Models of computations based on paradigms such as ant colony, bee colony, mimetic and differential-evolution algorithms.
PART B
Neural networks and neural computation. Biological and artificial neurons. Structure, basic operation, stimulation and activation function of the neuron. Training, learning and generalization. Methods for training neural networks. Supervised training. Unsupervised training. Reinforcement learning. Applications of neural networks in science and technology. Classification and regression problems and issues. Linear and non-linear classifiers. Neural networks as classifiers optimizing a cost function. Perceptron and multi-layer perceptron. Support vector machines. Probabilistic neural networks. Recurrent neural networks, Boltzman machines, time delay networks, radial basis function neural networks. Unsupervised learning, vector quantization and Kohonen self-organizing maps. Deep learning networks and applications. Statistical learning theory. Neural network output interpretation. Specific issues on cellular neural networks, artificial immune systems and membrane computing.