Machine Learning

Lecturer: 
Assistant: 
Objectives: 

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

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 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.

Description: 
  • Concept Learning
  • Decision Tree Learning
  • Bayesian Learning
  • Instance-Based Learning
  • Inductive Logic Programming
  • Evaluating Hypotheses
  • Computational Learning Theory
  • Reinforcement Learning
Examination: 

The exam is written, with one oral question, and open book.

There will be one project, a case study. This work is mandatory and should be handed in before the exam session in January. The exact deadline will be announced later. The result will be taken into account for the final score.

Material: 

Machine learning, Tom Mitchell.

The slides can be downloaded from Tom Mitchell's website. A version of the slides with extra information can be found on PointCarré website of this course.

You can find additional material, such as exercises and information on the project on the PointCarré website of this course.