Artificial Intelligence for Developers
In this course, you will learn how to work with machine learning in MATLAB, with a focus on practical applications within the MATLAB environment. You will build and train both supervised and unsupervised models using the Machine Learning Toolbox and learn how to prepare and analyze datasets. You will also learn how to evaluate and optimize models and present the results using MATLAB visualizations.
Within Matlab, Machine Learning (ML) is a powerful tool for analyzing data and developing models. In this course, you will work directly with Matlab to apply algorithms that recognize patterns and learn from data, without having to program everything manually. Matlab serves as the foundation for building reproducible and scalable analyses, supported by powerful computational functions and toolboxes. At Geo-ICT, we apply Machine Learning in Matlab to fully leverage the potential of geoinformation and geodata, so that insights can be directly translated into practical applications.
There are several subcategories within Machine Learning that you will apply in Matlab:
These techniques are applied within MATLAB to datasets and workflows that are directly relevant to analysis and modeling. From structuring data to building models and visualizing results: everything happens within a single integrated environment.
Whether you’re new to Machine Learning or want to deepen your Matlab skills, the “Machine Learning with Matlab” course teaches you how to implement ML concepts in Matlab. You’ll learn not only theory, but especially how to use Matlab to convert data into models and insights.
This course requires prior knowledge of the Matlab programming environment; if you do not have this knowledge, we recommend taking the Matlab Basics course.
Machine Learning in Matlab is a practical way to analyze data and build models using built-in algorithms and functions. At its core, you use Matlab to process data, train models, and evaluate results.
With Matlab, you can import, transform, and analyze datasets, after which you can immediately apply Machine Learning models. This includes using functions for classification, regression, and clustering, without having to implement complex algorithms yourself.
At Geo-ICT, we use Matlab to analyze geodata, for example by processing satellite data, analyzing time series, or recognizing patterns in spatial datasets. Matlab makes it possible to perform these analyses efficiently and reproducibly.
In this course, you will learn how to set up, train, and apply Machine Learning models in Matlab. From supervised and unsupervised techniques to evaluating model performance. The emphasis is on practical work with Matlab and building complete workflows.
Matlab is a powerful platform for developing and applying Machine Learning models. Thanks to the combination of mathematical functions, scripting, and specialized toolboxes, you can build models quickly and efficiently.
Here are some key points that underscore the importance of Matlab:
Some practical benefits of MATLAB in ML projects include:
You’ll learn the most important Machine Learning concepts and how they’re applied in Matlab, such as classification, regression, overfitting, and model validation. The focus is on understanding and immediately implementing these concepts in Matlab.
You will learn how to develop and apply models in Matlab:
The Machine Learning with MATLAB course focuses entirely on practical application within MATLAB and prepares you to work with data in technical and geospatial environments.
Choosing our Machine Learning with Matlab course at Geo-ICT means you’ll learn how to analyze data, build models, and apply results within a single powerful environment. This way, you’ll develop immediately applicable skills for working with geo-data and technical datasets.
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Contact usThe first day focuses on the fundamentals of machine learning in MATLAB and setting up an initial practical workflow. We begin with a brief introduction to the key ML concepts, immediately linking them to MATLAB functionality such as classification, regression, and clustering. Participants will gain insight into how these techniques are applied in practice using MATLAB functions and scripts within geodata.
Following this foundation, there will be a hands-on introduction to the Statistics and Machine Learning Toolbox. Participants will learn to import datasets (e.g., using `readtable`), edit them, and prepare them in MATLAB (tables and matrices). We will then apply supervised learning using functions such as `fitlm` (linear regression) and `fitclinear` or `fitcsvm` (classification), including splitting data into training and test sets and evaluating models.
The day concludes with practical exercises in which participants independently set up their first machine learning model in MATLAB. We will work with (geo)datasets, with an emphasis on completing a full workflow: from data import and preprocessing to model training and simple visualization of the results.
On the second day, the focus is on delving deeper into machine learning in MATLAB and expanding the workflow with more advanced techniques. We will work with unsupervised learning methods such as clustering (k-means) and dimensionality reduction (PCA), and be introduced to neural networks within the Deep Learning Toolbox. Participants learn how these techniques are applied in MATLAB to recognize patterns and structures in (geo)data.
Participants will apply these methods directly to realistic datasets, such as satellite images or sensor data. In doing so, they will work with MATLAB functions for data analysis and visualization, such as plotting clusters, feature spaces, and model outputs, so that both the input data and the results become clear.
In addition to technical depth, we focus on model optimization in MATLAB. Participants learn to deal with overfitting and underfitting and apply techniques such as cross-validation (crossval) and hyperparameter tuning to improve model performance.
The course concludes with a practical case study in which participants independently execute a complete machine learning workflow in MATLAB—from data import and preprocessing to model selection, training, and evaluation. This final assignment demonstrates how MATLAB can be effectively used to solve geoinformation problems.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
Yes, a basic understanding of MATLAB is required to get the most out of the course. We’ll dive right into the Statistics and Machine Learning Toolbox and the Deep Learning Toolbox, so some familiarity with the programming environment is necessary. If you don’t have this knowledge yet, we recommend taking our MATLAB Basics course first.
We primarily work with geospatial information and geodata, such as satellite imagery, sensor data, and time-series data. This way, you’ll learn how to apply machine learning in MATLAB to realistic and recognizable datasets that are relevant to geospatial challenges.
Both courses cover the same core concepts of machine learning. The difference lies in the programming environment: Python offers a broad ecosystem of open-source libraries, while MATLAB excels in integrated toolboxes, user-friendly visualization, and a strong presence in engineering and scientific applications. This course therefore focuses specifically on using MATLAB for machine learning projects.
By the end of the course, you will be able to independently import, prepare, and analyze datasets in MATLAB; build and evaluate supervised and unsupervised models; and visualize results. In addition, you will be able to apply machine learning techniques to solve geoinformation problems in your own field of work.
