Machine learning

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 >