Introduction to Machine Learning

Faculteit Science and Engineering
Jaar 2020/21
Vakcode WBCS032-05
Vaknaam Introduction to Machine Learning
Niveau(s) bachelor
Voertaal Engels
Periode semester I a

Uitgebreide vaknaam Introduction to Machine Learning
Leerdoelen At the end of the course, the student is able to:
1. Obtain an overview of the main methods and techniques used in pattern recognition and machine learning
2. Basic problems of machine learning and pattern recognition
3. Select appropriate techniques for particular tasks
4. Implement basic learning and validation methods and apply them to practical data sets.
5. Obtain insight into some basic operations in image processing
Omschrijving The focus of this course is to introduce the concepts of machine learning, including unsupervised and supervised learning. Unsupervised learning covers dimensionality reduction, clustering, and density-based estimation methods. Supervised learning is explained mainly in terms of classification problems, with emphasis on distance-based methods such as Learning Vector Quantization as an example framework. Validation methods and the problem of over-fitting are briefly discussed in terms of classification and regression problems. Methods are also illustrated by practical applications.
Uren per week
Onderwijsvorm Hoorcollege (LC), Practisch werk (PRC)
Toetsvorm Practisch werk (PR), Schriftelijk tentamen (WE)
(Final grade: weighted average of the marks for the practicals (60%) and the written (digital) exam (40%). In order to pass, students must get a mark of at least 5.5 for both of these separately, and the weighted average has to be at least 5.75. Weighted averages above 4.75 and below 5.75 will result in a final grade 5.0, in all other cases the grade is rounded to the nearest half-integer value.)
Vaksoort bachelor
Coördinator prof. dr. M. Biehl
Docent(en) prof. dr. M. Biehl ,dr. E. Talavera Martínez
Verplichte literatuur
Titel Auteur ISBN Prijs
All material is provided in the form of handouts and addtional information (links, articles etc.) in Nestor
Entreevoorwaarden Basic programming skills, knowledge of mathematics and statistics that is typical of the respective courses included in standard bachelor CS curricula.
Opmerkingen The computer practicals are in Matlab. It would be helpful if students have some programming experience in Matlab. Otherwise, they should quickly acquire some Matlab programming skills by following an on-line tutorial (many are available on internet). This is not difficult because Matlab is easy to learn on the background of some programming skills in another programming language.
The use of matlab will not be obligatory, students can resort to Python if they wish.


In the academic year 2020-2021, all CS bachelor courses have limited enrollment:
- CS students can always enter each course, regardless of whether the course is mandatory for them or not.
- A maximum of only 20 places per course is available for non-CS students. These places are filled on a first-come-first-served basis, with priority given to students with a strong CS-related background (e.g., CS exchange students, AI students, etc.). These students need to meet the course prerequisite requirements as mentioned on Ocasys.

For more info about the enrollment procedure, see
Opgenomen in
Opleiding Jaar Periode Type
BSc Artificial Intelligence 3 semester I a keuze
BSc Computing Science  (Optional electives for CS students) 3 semester I a keuze
BSc Courses for Exchange Students: AI - Computing Science - Mathematics - semester I a