Course Content:
- Learning of concepts
- Bayesian learning
- Instance based learning
- Inductive logic programming (learning of rules)
- Evaluation of hypotheses: confidence, bias and variance
- Computational learning theory
- Reinforcement learning
- Clustering
- Transparent and Trustworthy Machine Learning
Learning Outcomes:
- Knowledge and insight
The student is acquainted with a range of basic learning algorithms.
The student is capable of applying evaluation techniques to estimate the performance of the obtained model and to calculate the performance of an algorithm given a concrete application context using computational learning theory. - The use of knowledge and insight:
He 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 to devise and sustain arguments in favor or against some choice of learning technique for a given problem. - Communication
He/she can motivate the chosen approach to specialist and non specialists. - Skills
Students have obtained the skills to autonomously program, analyse, and apply learning techniques to a wide variety of problems.
All detailed and official information about the course here >
