- Brief introduction to AI (with focus on search spaces and planning)
- Learning of concepts (version spaces and decision trees)
- Instanced based learning
- Reinforcement Learning
- Bayesian Learning
- Neural Networks
- Evaluation of hypotheses: confidence, bias variance trade off
- Computational learning theory
- Evolutionary algorithms
- Knowlegde and insight:
The student is acquainted with the most commonly used AI techniques, including learning algorithms.
The student can correctly trace a learning algorithm on a small data set or environment.
The student is capable of applying evaluation techniques to estimate the performance of the obtained results and to calculate the performance of an algorithm given a concrete application context.
- The use of knowledge and insight:
The student can explain the outcome of a given algorithm when applied in a given setting.
The student is able to choose the appropriate techniques given a concrete learning problem, to apply them correctly and to evaluate the obtained results.
- Judgement ability:
The student must be able to devise and sustain arguments in favor of against some choice of (learning-) technique for a given problem.
He/she can motivate the chosen approach to specialist and non specialists.
Students have obtained the skills to autonomously develop, program, analyse, and apply learning techniques to a wide variety of problems.
All detailed and official information about the course here >