Adaptive Systems

Bernard Manderick
Course description
Course Content:
  • During this course we will watch a number of video MLSS lectures on a number of machine learning topics. MLSS stands for Machine Learning Summer School and is recurring summer school for PhD students in machine learning. The lectures are giving by top experts on the particular topic.
Learning Outcomes:
  • Overall, to acquire the necessary skills to understand and to make a synthesis of the state of the art topics in machine learning and complex dynamical systems. Knowledge and insight: After successful completion, the student should have knowledge and insight in the domain of machine learning and complex dynamical systems which allows her or him to provide an original contribution to the domain.
The corresponding competences:
  • The use of knowledge and insight: 
    The student is able to combine the ideas discussed in the course to tackle a new problem in an interesting way.
  • Judgement ability: 
    The student can make an independent judgement of papers in these scientific domains.
  • Making Judgements:
    Students have to be capable of determining whether a given problem lends itself to an implementation that is based on logic programming.
  • Communication:
    The student is able to present the papers reviewed during the course to fellow students and to formulate his own opinion concerning the content.
  • Skills:
    The student can search for, collect, read and synthesize papers in this area of research.

All detailed and official information about the course here >