Mathematics and Computer Science Master of Science an der Universität Augsburg

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 for Business Mathematics I)

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

Grundlagen von Datenstrukturen, Algorithmen und deren Anwendung in der Datenvorbereitung und Datenintegration. Behandelt Komplexitätsklassen, Laufzeitanalyse, graphentheoretische Algorithmen, dynamische Programmierung, Such- und Hashing-Algorithmen sowie Data Cleaning, Schema Matching und Data Matching.

Machine Learning

Methodologische Grundlagen der probabilistischen Maschinellen Lernens einschließlich Verteilungen, graphische Modelle, Maximum Likelihood, Bayessche Inferenz, Informationstheorie, EM-Algorithmus, Regression, Gaussian Processes, generative Modelle und State-Space Modelle.

Mathematical Methods in Data Science A

Fundamentale mathematische Konzepte in Maschinellem Lernen und Data Science wie Least Squares, Regression, Dimensionalitätsreduktion, SVD, konvexe und nichtkonvexe Optimierung, Gradient Descent, statistische Lerntheorie, Kernel-Methoden und Support Vector Machines.

Mathematical Methods in Data Science B

Fortgeschrittene mathematische Konzepte in Maschinellem Lernen einschließlich statistischer Lerntheorie, neuronaler Netzwerk-Modelle, Approximationstheoreme für Deep Learning und Reinforcement Learning.

Organic Computing II

Konzepte und Methoden des Organic Computing für komplexe vernetzte Systeme. Behandelt Self-Organisation, Self-Adaptation, Observer/Controller-Architekturen, Learning Classifier Systems, genetische Algorithmen und Particle Swarm Optimisation.

Practical Module Autonomous Driving

Praktischer Kurs zur Lösung komplexer Aufgaben in Konzeptdesign, Entwicklung und Absicherung hochautomatisierter/autonomer Fahrzeuge mittels modellbasierter Entwicklungsmethoden und aktueller Tools.

Process Mining

Umfassender Überblick über Process Mining mit Fokus auf Modellierungstechniken (Petri Nets, Workflow Nets), Discovering Algorithms (Alpha-Algorithm, Inductive Miner) und Conformance Checking.

Analysing Massive Data Sets

Konzepte und Methoden zur Analyse massiv großer Datenmengen einschließlich Information Retrieval, Ähnlichkeitssuche, Clustering, Link- und Netzwerk-Analyse sowie deren Implementierung.

Advanced Deep Learning

Detaillierte und aktuelle Kenntnisse im Maschinellen Lernen mit Fokus auf die Implementierung vollständiger Pipelines für multimodale Datenverarbeitung mit tiefen neuronalen Netzen.