Introduction to machine learning

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Introduction to machine learning 

Monday 23  - Friday 27 June 2025

 

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 Python), 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.

Programme
  • Day 1 - morning: Introduction to machine learning, methodology and best practices
  • Day 1 - afternoon: Introduction to Python, Practical on data 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 Ricardo da Silva Torres (Artificial Intelligence Group, Wageningen University & Research)
Dr Ioannis Athanasiadis (Artificial Intelligence Group, Wageningen University & Research)
Prior knowledge Basic skills in statistics are a plus. Practicals will be in Python. A short introduction will be provided on the first day, but previous programming experience in R or Python is required
Location Wageningen University Campus, Forum building room B0106
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 check Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Note that besides the restaurants in Wageningen, there are also options to have dinner on Wageningen Campus.
Fees 1
  EARLY-BIRD FEE 2 REGULAR FEE 
PE&RC / WIMEK / WASS / EPS / VLAG / WIAS PhD candidates with an approved TSP and WU EngD candidates € 300,- € 350,-
PE&RC postdocs and staff € 610,- € 660,-
All other academic participants € 650,- € 700,-
Non academic participants € 955,- € 1005,-

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 21 APRIL 2025

Note:

  • 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 the payment is arranged correctly and in time.
PE&RC Cancellation Conditions

IMPORTANT: ALWAYS read the Cancellation conditions for PE&RC courses and activities.

 

More information

PE&RC
Email: office.pe@wur.nl

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