Statistical Machine Learning

Professor
Bernard Manderick, Gianluca Bontempi
Course description
Course Content:
  • The Learning Problem
  • Is Learning Feasible?
  • The Linear Model
  • Error and Noise
  • Training versus Testing
  • Theory of Generalization
  • The Vapnik-Chervonenkis Dimension
  • Bias-Variance Tradeoff
  • Neural Networks
  • Overfitting
  • Regularization
  • Validation
  • Support Vector Machines
  • Kernel Methods
  • Bayesian Machine Learning
  • Causality
Learning Outcomes:

To introduce the basics of Machine Learning. The student has to be able to

  • Understand the basic ideas behind these techniques
  • Implement these techniques using the Python ecosystem
  • Apply these techniques to simple problems
  • Evaluate their performance.
The corresponding competences:
  • Knowledge and insight::
    After successful completion of the course the student should have insight into which problems can benefit from pattern recognition techniques and how to apply these techniques to the problem at hand. (S)he should also have insight in methodological issues involved.
  • Use of knowledge and insight:
    The student should be able to apply pattern recognition techniques and to tune the parameters of the chosen algorithm. The use of the Python Ecosystem should enable the student to write small programs to solve pattern recognition problems.
  • Judgement ability:
    The student should be able to judge the qualities of the different pattern recognition techniques and their results on the problem at hand.
  • Communication:
    The student should be able to communicate with experts about pattern recognition problems. (S)he should also be able to report and to present the results of his or her experiments to both specialists and non-specialists.
  • Skills:
    The student should be able to autonomously develop, program, analyze, and apply pattern recognition techniques to a wide variety of problems.

All detailed and official information about the course here >