Techniques of AI

Course Content:

• 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

Learning Outcomes:

• 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.
• Communication:
  He/she can motivate the chosen approach to specialist and non specialists.
• Skills:
  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 >