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The objective of this course is to provide knowledge in machine learning models and techniques. Both fundamentals as well as practical applications are discussed.
After a successful completion of this course, students will be able to:
This course introduces the fundamental methods of machine learning and statistical pattern recognition. This course will cover both theoretical foundations as well as implementation of machine learning in the data mining context. We will analyze data to create predictive and prescriptive models with (un)supervised machine learning methods, such as regression, clustering, tree based methods ensemble methods, support vector machines, (deep) neural networks, and Gaussian processes.
This course will introduce the end-to-end process of investigating data through machine learning methodology. The goal is either to discover / generate some preliminary insights in an area where there really was little knowledge beforehand, or to be able to predict future observations accurately. This includes methods to extract and identify useful features that best represent your data.
The sessions will focus on theoretical aspects of machine learning methods and algorithms, but also on hands-on experience using a suitable programming language. The course will not focus on particular applications. That is the objective of the optional project, where you can use the foundations provided for an application from your own scientific area. Examples of machine learning applications in the operations management and logistics field are provided during the sessions.
The material for this course includes selected papers and chapters from:
A selected list of papers will be made available at the beginning of the course.