lecturer of 2022/2023 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Brief description of the course
Introduction to the principles and algorithms used in the field of artificial intelligence. Solving problems by searching. Heuristics. Fundamentals of logical and probabilistic reasoning. Bayes' rule. Machine learning: supervised learning, decision tress, classifying with linear models and reinforcement learning. Neural networks. Ethics of artificial intelligence.
Learning outcomes in the course
Upon completing the course the student:
- is capable of formulating artificial intelligence problems as state space search;
- knows tree search and local search algorithms and can apply them; including BFS, DFS, A* and hill climbing;
- can describe the behaviour and parameters of tree search and local search;
- is familiar with modern approaches to combinatorial search (metaheuristics);
- is knowledgeable about the principles of logical and probabilistic reasoning;
- can solve problems involving propositional logic and Bayesian probability;
- is familiar with some problems in machine learning (classification, learning behaviour) and approach methods (decision tree, linear classifiers, neural networks, reinforcement learning);
- can select and apply a suitable machine learning method;
- can use a modern machine learning package to solve machine learning tasks (scikit-learn or weka).