Mathematical Foundations of Big Data Analytics (Lecture)

Lecturer (assistant)
Number0000004661
TypeLecture
Duration2 SWS
TermWintersemester 2020/21
Language of instructionGerman
Position within curriculaSee TUMonline
DatesSee TUMonline

Dates

Admission information

Objectives

At the end of this module, the students have a theoretical understanding of methods and algorithms for big data analysis. Moreover, the student are familiar with the main methods and algorithms for extracting hidden structures in big data, for analyzing high-dimensional data, for regression analysis, and they know adaptive algorithms of machine learning. They can decide which methods are applicable for different applications such as signal processing, image classification, or clustering.

Description

Data analytics is the problem of finding models for (big) data in order to extract information, draw conclusions, and make decisions. This lecture gives a detailed introduction in methods and algorithms for finding these data models and for a corresponding discussion making based on these data models. In particular, this lecture is divided into the following sections: Matrix analysis, multivariate distributions and moments, dimensionality reduction (principal component analysis, multidimensional scaling, non-linear methods), classification and grouping (discriminant analysis, clustering), support vector machine, machine learning.

Prerequisites

Basic knowledge in probability theory and stochastic processes, good working knowledge in linear algebra and analysis.

Teaching and learning methods

Presentation of the main content on the blackboard. Applying the learned theory to concrete Problems by solving exercises or/and working on small projects during the exercises.

Examination

There will be a written exam. In this exam, the students have to answer questions and they have to explain approaches to solve a given problem. In doing so, the students should show that they understand the methods and algorithms learned in this module and that they can apply the learned methods to concrete problems.

Recommended literature

Lecture notes and recommendation for further readings will be provided in the lecture.

Links