Machine learning

Professor
Ann Nowé
Course description
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
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 >