Machine Learning

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.

This course discusses the theory of different methods for regression, classification, and clustering and the application thereof in different fields of agricultural and life sciences. Students will learn how to properly train and evaluate machine-learning models on data, what typical issues are that can arise, and how to deal with these. Furthermore, attention is paid to the ethical, legal, and social aspects of applying machine learning in practical use-cases.

During the course, every day is dedicated to a specific topic, with a lecture and pen-and-paper exercises in the morning, and a practical session with computer exercises in the afternoon. In addition, there are four project days, where students work in pairs to create a solution for practical use-cases.

Note: there is a significant overlap with the course Statistics for Data Scientists (MAT32806). We recommend students to not take both courses. Machine Learning is recommended if you intend to take the courses Deep Learning (AIN31306) and/or Advanced Machine Learning (FTE36806).

• Explain machine-learning problems, algorithms, and their formulas

• Qualitatively and quantitatively compare the characteristics, (dis)advantages, formulas, and performance of a number of key algorithms

• Evaluate a use-case to choose the right machine-learning method and to properly analyse the results

• Apply machine-learning techniques in a) biosystems engineering (MBE), b) bioinformatics (MBF), c) geo-information sciences and remote sensing (MGI), or another field of study

• Design and implement effective solutions based on chosen algorithms, to solve practical problems

• Written test with open and closed questions (50%) This is a closed-book exam with closed and open questions
• Assignment other (50%) Students work in groups of two. The project grade is based on the quality of the submitted report, project code, demonstration and explanation. In case of a failed project, a resubmission can be handed in during the resit periods.

• Mathematics (Mathematics 1 (MAT14803) and Mathematics 2 (MAT14903), or equivalent);
• Statistics (Data Analysis Biosystems Engineering (FTE26306), Advanced Statistics (MAT20306), or equivalent);
• Programming in Python (INF22306).

• James G., Witten D., Hastie T., Tibshirani R., and Taylor, J.: An Introduction to Statistical Learning: with Applications in Python (ISBN: 978-3031387463, freely available online at https://www.statlearning.com)