This course provides an introduction to machine learningwith a special focus on engineering applications. The course starts with amathematical background required for machine learning and covers approaches forsupervised learning (linear models, kernel methods, decision trees, neuralnetworks) and unsupervised learning (clustering, dimensionality reduction), aswell as theoretical foundations of machine learning (learning theory,optimization). Evaluation will consist of mathematical problem sets andprogramming projects targeting real-world engineering applications.
