Microalgae produce a variety of interesting ingredients and are therefore an ideal source for food, feed, cosmetics and fine chemicals. Although the basic mechanism of microalgae growth has been well studied, there are only a few mathematical models that can be used to model microalgae growth. Such models are particularly important for the large‑scale cultivation of microalgae and serve as a basis for a robust, predictive control system. An essential component of this system are algorithms that enable automated optimization of microalgae growth. So‑called machine learning has been widely used for prediction and optimization in different areas. To predict the growth behavior of the microalgae Phaeodactylum tricornutum in outdoor cultivation, so-called Support Vector Machines (SVM) were used. The results show that the SVM‑based model can predict the growth rate of Phaeodactylum tricornutum with a correlation coefficient of 88 percent. At the same time, a model with Monod kinetics yields a correlation coefficient of 82 percent. These two models will be further validated on both laboratory and pilot scale in order to establish a model‑predictive control for microalgae production.