Neural Networks and Learning System

Aim

After completion of the course the student should on an advanced level be able to: - design and apply artificial neural networks and similar methods for signal, image and data analysis that learn from previous experience and data - apply methods to find meaningful relations in multidimensional signals where the degree of complexity makes traditional model-based methods unsuitable or impossible to use - explain the difference between particular learning paradigms, implement and use common methods in those paradigms and select an appropriate method for solving a given problem

Content

Machine learning, classification, pattern recognition and high-dimensional data analysis. Supervised learning: neural networks, linear discriminants, support vector machines, ensemble learning, boosting. Unsupervised learning: patterns in high-dimensional data, dimensionality reduction, clustering, principal component analysis, independent component analysis. Reinforcement learning: Markov models, Q-learning.