De elektronische studiegids voor het academiejaar 2025 - 2026 is onder voorbehoud.





Machine Learning (4561)
  
Coordinating lecturer :Prof. dr. Dirk VALKENBORG 
  
Co-lecturer :Prof. dr. Inigo BERMEJO DELGADO 

Language of instruction : English
Credits: 5,0
  
Period: semester 1 (5sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   Mandatory sequentiality bound on the level of programme components
 
 
Group 1
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Probability and Statistics (1798) 5.0 stptn
    Linear Models (3560) 5.0 stptn
    Programming in R (4406) 3.0 stptn
 
Or group 2
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Probability and Statistics (1798) 5.0 stptn
    Linear Models (3560) 5.0 stptn
    Programming in Python (3306) 5.0 stptn
 

Prerequisites

The student has knowledge about basic statistics and mathematics, such as, e.g., maximum likelihood estimation, linear regression, binary classification, matrix algebra, optimization. The student can program in R or Python.


Content

In this coursework we give a non-exhaustive overview of the basic principles of machine learning. In several classes we cover topics like, bias/variance trade off, simple linear regression and classification, cross validation and bootstrapping, unsupervised methods, feature selection methods, splines, random forests, and support vector machines. The theory is applied on a Kaggle competition. The course aims at junior level data scientists. Notion of programming and mathematics/statistics is mandatory.

- A framework for machine learning

- Simple supervised methods: Linear regression and Classification

- Resampling methods, Model selection and Regularization

- Moving beyond simple supervised methods:

  • Regression splines
  • Local regression
  • Generalized additive models
  • Tree-based method
  • Support Vector Machines

- Unsupervised techniques


Organisational and teaching methods
Organisational methods  
Project ✔  
Self-study assignment ✔  


Evaluation

Period 1    Credits 5,00

Evaluation method
Written evaluaton during teaching periode25 %
Transfer of partial marks within the academic year✔
Conditions transfer of partial marks within the academic yearThe student needs to pass this component of evaluation.
Peer review✔
Report✔
Oral evaluation during teaching period25 %
Transfer of partial marks within the academic year✔
Conditions transfer of partial marks within the academic yearThe student needs to pass this component of evaluation.
Debat✔
Presentation✔
Written exam50 %
Closed-book ✔
Open questions✔
Evaluation conditions (participation and/or pass)✔
Conditions A student must at least attend all components of the evaluation. A student must obtain a tolerable exam result (≥8/20) for each component to be able to pass the programme component.
Consequences If a student does not attend one of the evaluation components, he/she will receive an 'X' for the programme component. If the student achieves less than 8/20 in part of this programme component, this lowest partial grade will be the final grade for the entire programme component for the examination opportunity concerned.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory textbooks (bookshop)
 

Textbook 1:

An Introduction to Statistical Learning with Applications in R, James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013, Springer-Verlag, (e-copy freely available online)
 

Compulsory course material
 

Lecture notes will be made available at Blackboard.
 

Recommended reading
  The Elements of Statistical Learning,Hastie, T., Tibshirani, R. and Friedman, J.,2009,Springer-Verlag,Available as e-book: https://link.springer.com/book/10.1007%2F978-0-387-84858-7


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

  •  EC 

    • The student can critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is able to efficiently acquire, store and process data.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

    • The student knows the international nature of the field of statistical science and data science.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
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Exchange Programme Statistics J



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