# What is mathematical analysis

### Mathematical methods for processing and analyzing mass data (exercise)

### Events

- 02.03.2020 13: 30-16: 00 0534, small lecture hall
- 03.03.2020 13: 30-16: 00 0534, small lecture hall
- 04.03.2020 13: 30-16: 00 0534, small lecture hall
- 05.03.2020 13: 30-16: 00 0534, small lecture hall
- March 6th, 2020 13: 30-16: 00 0534, small lecture hall
- 09.03.2020 13: 30-16: 00 0534, small lecture hall
- 03/10/2020 13: 30-16: 00 0534, small lecture hall
- 11.03.2020 13: 30-16: 00 0534, small lecture hall
- 12.03.2020 13: 30-16: 00 0534, small lecture hall
- 13.03.2020 13: 30-16: 00 0534, small lecture hall

### Eligibility criteria

See TUMonline

### learning goals

After successfully participating in this event, the students know and master the essential methods for recognizing hidden structures in mass data, methods for analyzing high-dimensional data, data-based model adaptation processes, as well as adaptable algorithms and methods of machine learning. Furthermore, they are familiar with how these methods are used in different applications, for example in signal processing, image classification, affiliation recognition, etc.

### description

The aim of analyzing large amounts of data (“Big Data”) is to find models for this data in order to be able to draw appropriate conclusions or findings and make decisions. This event introduces the theoretical basics of this "big data" analysis and presents the most important algorithms and methods for data analysis. The content of the lecture is divided into the following sections: matrix calculation, multivariate distributions and moments, dimension reduction (principal component analysis, multidimensional scaling, non-linear methods), classification and grouping (discriminant analysis, cluster analysis), support vector machine, machine learning.

### Content requirements

Basics of probability theory and stochastic processes, good knowledge of analysis and algebra.

### Teaching and learning methods

Development and presentation of the lecture content on the blackboard. Consolidation of the lecture material by solving tasks and calculation examples in the exercises.

### Study and examination performance

The module examination is carried out in the form of a written examination. In this, by answering questions and presenting a solution approach for a given problem, it should be proven that the students know the essential algorithms and methods from the lectures and exercises and can use them in corresponding applications.

### Recommended literature

A lecture notes will be provided. Further literature will be given in the lecture.

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