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Statistical foundations of machine learning

academic year

Course teacher(s)

Gianluca BONTEMPI (Coordinator)

ECTS credits


Language(s) of instruction


Course content

(1) Foundations of statistical modelling, (2) parametric estimation, (3) nonparametric estimation and resampling, (4) supervised learning ( model selection, variable selection), (5) algorithms for regression (neural networks, local learning, (6) classification algorithms (KNN, Naive- Bayes, SVM), (vii) applications of machine learning (data mining, text mining, web mining)

Objectives (and/or specific learning outcomes)

Statistical machine learning is the discipline which aims at extracting knowledge and inferring predictive models from observed data. The course will focus on the statistical notions (like bias, variance, regression, validation) which are necessary to create, identify and assess a predictive model. This course aims to find a good balance between theory and practice by situating most of the theoretical notions in a real context with the help of illustrative case studies (from biology, finance, medicine) and real datasets.

Prerequisites and Corequisites

Required and Corequired knowledge and skills

  • Basic notions of probability and estimation (bias, variance)
  • Linear algebra and numerical analysis (linear systems, eigenvalues)
  • Least-squares
  • Programming

Cours ayant celui-ci comme co-requis

Teaching methods and learning activities

Lectures, exercises and practical assignments. All the assignments involve practical work using the software package R.

Contribution to the teaching profile

  • Analysis and mathematical modelling of information
  • Collect, analyse, discuss and interpret data
  • Learning of new concepts
  • Design a modelling procedure
  • Critical analysis of the results wrt state-of-the-art
  • Operation knowledge of English
  • Conceive a structural solution and algorithms to solve a problem
  • Implement a prototype
  • Learning of R statistical software

References, bibliography, and recommended reading

  • G. Bontempi "Statistical foundations of machine learning: the handbook"

  • L. Wasserman (2004) All of statistics: a coincise course in statistical inference. Springer.

  • R. O. Duda, P.E. Hart, D. G. Stork (2001) Pattern Classification. Wiley.

  • T. Hastie, R. Tibshirani, J. Friedman (2001) The elements of statistical learning: data mining, inference, and prediction. Springer.

Course notes

  • Syllabus
  • Université virtuelle

Other information

Additional information

All information on the UV page.



Email: Gianluca.Bontempi@ulb.be

Office: Campus La Plaine, NO8-107

Postal address: Département d'Informatique, Bld de Triomphe, CP 212




Method(s) of evaluation

  • written examination
  • Project

written examination


Project (in R language) and written exam on theoretical aspects of the course. The written exam (on the UV platform) will require as well the usage of the R software to answer questions.

Mark calculation method (including weighting of intermediary marks)

  • 10/20 (project)
  • 10/20 (UV written exam about theory requiring the use of the R software)

Language(s) of evaluation

  • english
  • (if applicable french )