Machine Learning

What it is about

You will learn the probabilistic foundations of machine learning and central learning algorithms — from simple neighborhood and clustering methods to linear models and support vector machines up to neural networks and the EM method. By the end you will be able to select, describe and derive suitable algorithms for given problems.

What you will be able to do

• Understanding probabilistic foundations of Machine Learning
• Knowledge of essential learning algorithms (supervised & unsupervised)
• Selection of suitable algorithms for concrete problem settings
• Description and derivation of selected procedures

What the module consists of

• LectureCovers the theoretical topics of the module
• ExercisesApplication and deepening of the lecture content
• HomeworkSelf-study to deepen the topics

Teaching method

• Flipped ClassroomEncourages active learning; lecture content is prepared and deepened in class
• Lecture slides and videosProvision of the core content for self-study