Continuous Optimization in Data Science

Course content

The students understand the of the complex links between their previous mathematical knowledge and the contents of the lecture, understand the theoretical body of the lecture as a whole and master the corresponding methods, are able to analyze and apply the methods of the lecture - remember and understand exemplary problems in Data Science master selected problem solving abilities using methods of continuous optimization and are able to apply them, understand theory and algorithms of continuous optimization in the context of statistical phenomena of the data basis.

Content

• Linear and Nonlinear Regression
• Matrix Completion
• Low Rank Parameterization
• Nonnegative Matrix Factorisation
• Sparse Inverse Covariance
• Sparse Principal Component Analysis
• Nichtlineare Support Vector Machines
• Logistic Regression
• Deep Learning
• selected applications