Machine Learning


Course Objective

Upon completion of this course, students will:
-get acquainted with the dominant concepts of machine learning methods,
including some theoretical background. (Knowledge and understanding)
-acquire knowledge of established machine learning techniques such as
Linear Models, Support Vector Machines, Decision Trees and Neural
Networks. (Knowledge and understanding)
-learn some statistical techniques to assess and validate machine
learning results. (Apply knowledge and understanding) (Make judgements)

Course Content

Machine Learning is the discipline that studies how to build computer
systems that learn from experience. It is a very active subfield of
Artificial Intelligence that intersects with statistics, cognitive
science, information theory, and probability theory. Recently, Machine
Learning has gained great importance for the design of search engines,
robots, and sensor systems, and for the processing of large scientific
data sets. Further applications include handwriting or speech
recognition, image classification, medical diagnosis, stock market
analysis and bioinformatics.

Teaching Methods

The course consists of two parts: a written exam and a practical
assignment. The written exam is supported by lectures (two per week) and
optional homework assignments (one per week). The practical assignment
is supported by small exercises to help with the relevant technologies,
and informal biweekly presentations. The practical assignment is made in
groups of five. No resit is possible for the practical assignment.

The course will be taught in English.

Method of Assessment

Exam (50%) and a project report (50%).


Some reading material will be provided digitally.

Target Audience

B Econometric and Operation Research optional Courses
B Information Science year 3 compulsory courses
B Business Analytics year 3 compulsory courses
B Computer Science year 3 compulsory courses
B Artificial Intelligence year 3 compulsory courses
M Bioinformatics and Systems Biology optional courses
B Artificial Intelligence year 3
B AI track Intelligent Systems

Recommended background knowledge

We recommend that students have some prior experience with Linear
Algebra, Calculus (limited to differentiation), and Probability Theory
(or Statistics). A basic understanding will suffice and we will take
some time to go over the basics again. Feel free to register if you have
no experience with any of these, but expect to put in a little extra
effort in the first weeks.
Programming experience, preferably in python, is highly recommended, but
not strictly necessary.

General Information

Course Code X_400154
Credits 6 EC
Period P4
Course Level 300
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. P. Bloem
Examiner dr. P. Bloem
Teaching Staff

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture
Target audiences

This course is also available as: