Modul

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• Students acquire knowledge of the basic methods of Machine Learning
• Students acquire the mathematical knowledge to understand the theoretical foundations of Machine Learning
• Students can categorize, formally describe and evaluate methods of Machine Learning
• Students can apply their knowledge to select appropriate models and methods for selected problems in the field of Machine Learning.

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The field of Machine Learning has made enormous progress in recent years and good knowledge of Machine Learning is becoming increasingly in demand on the job market. Machine Learning describes the acquisition of knowledge by an artificial system based on experience or data. Rules or certain calculations no longer have to be manually coded but can be extracted from data by intelligent systems.

This lecture provides an overview of essential and current methods of Machine Learning. After reviewing the necessary mathematical background, the lecture primarily deals with algorithms for classification, regression, and density estimation, with a focus on the mathematical understanding of probabilistic methods and neural networks.

Examples of topics include:
-    Basics in Linear Algebra, Probability Theory, Optimization and Constraint Optimization 
-    Linear Regression
-    Linear Classification
-    Model Selection, Overfitting, and Regularization
-    Support Vector Machines
-    Kernel Methods
-    Bayesian Learning and Gaussian Processes
-    Neural Networks
-    Dimensionality Reduction
-    Density estimation
-    Clustering
-    Expectation Maximization
-    Graphical Models

See partial achivements (Teilleistung)

180h, aufgeteilt in: 
•    ca 45h Vorlesungsbesuch
•    ca 15h Übungsbesuch
•    ca 90h Nachbearbeitung und Bearbeitung der Übungsblätter
•    ca 30h Prüfungsvorbereitung