Data Science Bachelor of Science an der Universität Augsburg

Organic Computing II

Teaches self-organization, self-adaptation, and nature-inspired algorithms including genetic algorithms, particle swarm optimization, and learning classifier systems for complex autonomous systems.

Practical Module Autonomous Driving

Practical course teaching conceptual design, development, and safeguarding of highly automated vehicles using model-based development methods and tools.

Process Mining

Covers process mining concepts including event logs, Petri nets, process discovery, conformance checking, and their application to business processes.

Analysing Massive Data Sets

Teaches concepts and methods for analyzing large datasets including information retrieval, similarity search, clustering, link analysis, and recommender systems.

Advanced Deep Learning

Provides detailed knowledge in machine learning and implementation of complete pipelines for multimodal data processing with deep neural networks.

Software-Intensive Systems

Real-Time Systems

Machine Learning and Computer Vision

Advanced Machine Learning and Computer Vision

Embedded Hardware

Isabelle-Lab

Quantum Algorithms

Biophotonics

Computer Vision for Intelligent Systems

Foundation Models in Deep Learning

Medical Monitoring and Advanced Sensor Data Processing

Search Engines and Neural Information Retrieval

Embodied Artificial Intelligence

Graph Algorithms

Trustworthy Artificial Intelligence

Advanced Biomedical Systems Modeling and Data Science

Algebraic geometry I

Algebraic geometry II

Riemannian Geometry

Differential Topology

Algebraic Topology

Calculus of Variations

Nonlinear Partial Differential Equations

Stochastic Differential Equations

Dynamical Systems

Control Theory

Numerical analysis of partial differential equations

Multiscale Methods

Mathematical modelling

Combinatorial Optimization

Discrete Mathematics (Optimisation IV)

Mathematical Statistics (Stochastics III)

Probability IV

Optimisation IV (Global Optimisation)

Parabolic Partial Differential Equations

Entropy and Information

Introduction to Projective Geometry

Introduction to Cryptography

Nonlinear Control Theory

Coding Theory

Lie Groups and Their Representations

Graph Theory

Financial Optimisation

Numerics of Stochastic Differential Equations

Parametric Optimisation

Numerical Optimisation Methods for Business Mathematics

Numerical Methods of Financial Mathematics

Design Theory

Continuous Time Finance

Time Series Analysis

Stochastic Evolution Equations

Adaptive Finite Element Methods

Symplectic Geometry

Stochastic Models for Financial and Energy Markets

Markov Chains and Monte-Carlo-Simulation

Ergodic Theory and Random Dynamical Systems

Bayesian Statistics and Econometrics

Advanced Methods in Machine Learning

Advanced Methods in Machine Learning II

Floer Homology

Advanced Survival Analysis

Nonparametric Statistics

Computational Algebraic Geometry

Inverse Problems

Advanced Discrete Probability

Homotopy Theory

High-Dimensional Probability with Applications to Data Science

Complex Geometry I

Lie Groups and Homogeneous Spaces

Mathematical Physics

Selected Topics in Mathematical Data Science

Project Module Software Methodologies for Distributed Systems

Project Module Databases and Information Systems

Project Module Resource Aware Algorithmics

Project Module Quantum Algorithms

Project Module Computational Linguistics

Project Module Intelligent Perception in Technical Systems

Project Module Data Engineering

Mathematics Project

Mathematics Seminar

Seminar Multimedia Computing & Computer Vision (MA)

Seminar Database Systems Master

Seminar Software Engineering of Distributed Systems (MA)

Seminar Software Engineering in Safety- and Security-Critical Systems (MA)

Seminar Quantum Algorithms (Master)

Seminar Generative Artificial Intelligence

Seminar Natural Language Understanding (Master)

Seminar Current Topics in Embodied Artificial Intelligence and Computer Vision

Seminar Data Engineering Master

Seminar Software Engineering for Artificial Intelligence Systems Master

Seminar Digital Ethics (Master)

Ethics & Computer Science

Master Thesis with Colloquium

Algorithms & Data Engineering

Teaches basic principles of algorithms, data structures, and their application in data engineering including data preparation, cleaning, schema matching, and data matching.

Machine Learning

Covers probabilistic machine learning methodologies including distributions, graphical models, Bayesian inference, neural networks, Gaussian processes, and deep generative models.

Mathematical Methods in Data Science A

Covers fundamental mathematical concepts in machine learning including least squares, regression, dimensionality reduction, optimization, statistical learning theory, and kernel methods.

Mathematical Methods in Data Science B

Addresses advanced topics including statistical learning theory, neural network models, approximation theorems, and reinforcement learning for data science.