4.BDT: Introduction to Machine Learning

Code
L.24298
Amount of hours required
140
Quartile of execution
1

This module focuses on the basic principles of data analysis and machine learning. The module covers topics linked to the machine learning process, the underlying mathematics and various machine learning techniques related to classifications.

Competences

HBO ICT 6.3 OP/ANA/3
HBO ICT 14.3 SW/REA/3
HBO-ICT 27.3 PS/OP/OPL

Learning goals

The student understands the basic concepts of differentiation and optimisation.
The student understands basic statistical concepts (various measurements such as nominal, ordinal, interval and ratio, median, modus, mean, standard deviation, spread, boxplot), and can apply these terms to any given data.
The student understands and can apply basic linear algebra concepts (matrices, vectors, matrix multiplication, inverse, transpose, in product, unit matrix).
The student understands machine learning processes and can apply this to a dataset.
The student can collect features from images using simple computer vision techniques.
The student can analyse a given dataset and determine what the best features are (i.e. features with the best distribution).
The student is able to perform data preprocessing.
The student is familiar with a range of machine learning algorithms, and can apply these.
The student can apply cross-validation techniques to overcome issues of overfitting and underfitting.
The student can export their own trained and tested model and implement it in a (small) application.

Tests

Code	Name
WC	Werkcollege
T.50353	4.BDT: Introduction to Machine Learning