FTE-35306 Machine Learning

Course

Credits 6.00

Teaching methodContact hours
Lecture20
Tutorial12
Practical58
Course coordinator(s)dr. GW Kootstra
Lecturer(s)prof. dr. ir. D de Ridder
dr. ir. S van Mourik
dr. ADJ van Dijk
dr. GW Kootstra
Examiner(s)dr. GW Kootstra
prof. dr. ir. D de Ridder
dr. ir. S van Mourik
dr. ADJ van Dijk

Language of instruction:

English

Assumed knowledge on:

mathematics (Mathematics 1; MAT-14803 and Mathematics 2; MAT-14903, or equivalent), statistics (Data Analysis Biosystems Engineering; FTE-26306, Advanced Statistics; MAT-20306, or equivalent), and some familiarity with computer programming (e.g. Programming in Python; INF-22306).

Contents:

Machine Learning deals with algorithms that predict certain outputs (such as crop yields or traits) given previously unseen input data from cameras, other sensors, maps, molecular measurements etc. These algorithms learn how to do so using training data (sets of input examples, usually with corresponding outputs). Machine learning plays an increasingly important role in many scientific areas, including biosystems engineering and bioinformatics. This course discusses regression, classification, clustering, and supporting methods.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- clearly explain machine learning problems, algorithms, and their formulas;
- understand well how machine learning can be used in a) biosystems engineering (MAB: data mining, precision farming, decision support systems, computer vision, agricultural robotics), and/or b) bioinformatics (MBF: data mining, molecular diagnostics, function inference, interaction prediction), and/or c) another field of study;
- qualitatively and quantitatively compare the characteristics, (dis)advantages, formulas, and performance of a number of key algorithms;
- design and implement effective solutions based on chosen algorithms, to solve practical problems.

Activities:

Interactive lectures, self-study, pen-and-paper exercises, computer exercises, and project work.

Examination:

The final grade will be determined by your project grade (50%, based on the quality of the submitted project code and report) and your written exam grade (50%, based on the quality of your written exam). For both, a minimum grade of 5.5 is required. If your written exam grade is below 5.5, you can try to improve it during one of the re-exam opportunities. If your project grade is below 5.5, you can submit a new version in the same week as one of the re-exam opportunities. The written exam is not an open book exam, but you will be provided with a formula sheet. During the 4 project days, you work in pairs. Your performance directly impacts the grade of your partner, as you will both obtain the same grade. On these 4 project days, attendance is obligatory. At the end of each project day, 2 pairs will give a short presentation.

Literature:

James G, Witten D, Hastie T, Tibshirani R: An introduction to statistical learning (ISBN978-1-4614-7138-7, freely available online), supported by various other sources.

ProgrammePhaseSpecializationPeriod
Restricted Optional for: MABBiosystems EngineeringMSc4WD
MBFBioinformaticsMSc4WD
MBFBioinformaticsMSc4WD