Introduction to machine learning

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Introduction to machine learning (online)

Monday 28 June - Friday 2 July 2021

- Registration is closed - 


sem.jpgMachine learning plays an increasingly important role in many scientific areas, including geo-information science and remote sensing, ecology, biosystems engineering and bioinformatics. Today, scientific data are growing in complexity, size, and resolution, and scientists are challenged to leverage available data to inform decision making. In this course, you will learn how to model patterns and structures contained in data, and evaluate data-driven models, i.e. models that learn directly from observations the phenomena under study. 
The course will focus on the following topics:

  • The machine learning methodology, and framing scientific problems as machine learning tasks
  • Data preparation and representation
  • Key algorithms for regression, classification, and clustering
  • Qualitative and quantitative comparison of characteristics, (dis)advantages, and performance of a number of key algorithms
  • Design and implementation of effective solutions based on chosen algorithms to solve practical problems

Through a series of lectures and practical exercises (in R), the participants will learn about different strategies and their pertinence for specific problems in environmental sciences,  but the course will remain general for a broader audience. Participants are encouraged to bring their own problems in class and analyse data from their own research.

  • Day 1 - morning: Introduction to machine learning, methodology and best practices
  • Day 1 - afternoon: Introduction to R scripting, Practical on d ata preparation and representation, cross validation, training/test splits 
  • Day 2 - morning: lecture on regression methods: linear, LASSO, feature selection, trees, neural networks
  • Day 2 - afternoon: practical on regression methods  
  • Day 3 - morning: lectures on classification methods: Bayesian, kNN, logistic, SVMs, ensembles, forests 
  • Day 3 - afternoon: practical on classification methods
  • Day 4 - morning: lectures on unsupervised analysis: hierarchical, k-means, EM, PCA, t-SNE 
  • Day 4 - afternoon: practical on unsupervised analysis 
  • Day 5 - morning: Bring your own data – Frame your science question as a learning task and work with own data
  • Day 5 - afternoon: Feedback/ discussion – Outlook on advanced/current topics (i.e. deep learning)  
General information
Target Group The course is aimed at PhD candidates, postdocs, and other academics that are interested in machine learning applied to environmental data
Group Size Min. 15 / Max. 20 participants
Course duration 5 days
Language of instruction English
Frequency of recurrence To be determined
Number of credits 1.5 ECTS
Lecturers Dr Ioannis Athanasiadis (Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research)
Dr Gert Kootstra (Farm Technology, Wageningen University & Research)
Dr Aalt Jan van Dijk (Bioinformatics, Wageningen University & Research)
Prof. Dick de Ridder (Bioinformatics, Wageningen University & Research)
Prior knowledge Basic skills in statistics are a plus. Practicals will be in R. A short introduction will be provided on the first day, but previous programming experience in R or Python is required
Location Wageningen University Campus, if Corona measures allow this. The final decision on whether the course will be on WUR campus will be made before the term of free cancellation expires (so before 30 May 2021) 
Options for accommodation Accommodation is not included in the fee of the course, but there are several possibilities in Wageningen. For information on B&B's and hotels in Wageningen please visit Another option is Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Finally, there are a number of groups on Facebook where students announce subrent possibilities and things like that. Examples include: Wageningen Room Subrent, Wageningen Room Sublets, Room Rent Wageningen, and Wageningen Student Plaza. Note that besides the restaurants in Wageningen, there are also options to have dinner on Wageningen Campus.
Fees 1
PE&RC / WIMEK / WIAS / WASS / EPS / VLAG PhD candidates with an approved TSP €280,- €330,-
a) All other PhD candidates
b) Postdocs and staff of the above-mentioned Graduate Schools
€700,- €750,-
All others €980,- €1030,-

1 The course fee includes a reader, coffee/tea, and lunches. It does not include accommodation (NB: options for accommodation are given above)
2 The Early-Bird Fee applies to anyone who REGISTERS ON OR BEFORE 28 APRIL 2021


  • If you need an invoice to complete your payment, please send an email to, including ALL relevant details that should be mentioned on the invoice (e.g., purchase order no., specific addresses, attendees, etc.).
  • The Early-Bird policy is such that the moment of REGISTRATION (and not payment) is leading for determining the fee that applies to you.
  • Please make sure that your payment is arranged within two weeks after your registration.
  • It is the participant's responsibility to make sure that he/she (or his/her secretary) completes the payment correctly and in time.
PE&RC Cancellation Conditions
  • Up to 4 (four) weeks prior to the start of the course, cancellation is free of charge.
  • Up to 2 (two) weeks prior to the start of the course, a fee of € 280,- will be charged.
  • In case of cancellation within two weeks prior to the start of the course, a fee of € 700,- will be charged.
  • If you do not show at all, a fee of € 980,- will nevertheless be charged.

Note: If you would like to cancel your registration, ALWAYS inform us and do not assume that by NOT paying the participation fee, your registration is automatically cancelled, because it isn't (and do note that you will be kept to the cancellation conditions).

More information

Dr. Sabine Vreeburg (PE&RC)
Phone: +31 (0) 317 489853


To register, please enter your details below and click "Register".