General Information
Here you will find the lectures that are offered on a Bachelor level.
If you would like to attend a Seminar or write your Bachelor Thesis at our chair, please participate in the allocation procedure, organized by Dr. Jürgen Blank.
Summer Term
Management Science (Operations Research)
Content
- Management Science 1 (Operations Research 1):
- Introduction to Operations Research: planning models and methods.
- Networks, graphs, and their applications: Paths in graphs, networks, flows in graphs, transportation problem
- Linear Optimization: basic model structures, Simplex algorithm, special LP structures, different phases of Simplex, variables with lower and upper bounds, post-optimality analysis (sensitivity analysis), parametric optimization, applications and modeling, duality.
- Integer linear optimization: Examples and case studies, Branch & Bound, Cutting Planes.
- Stochastic Processes: Queueing theory, simulation of stochastic processes.
- Management Science 2 (Operations Research 2):
- Nonlinear Optimization: Unconstrained and constrained nonlinear problems and models, Convex optimization, Karush-Kuhn-Tucker-conditions, lagrangian method, Quadratic optimization (Wolfe’s Algorithm), approximation methods (Golden Section, Gradient Method), Barrier methods, Penalty methods.
- Heuristics: problem search methods (A*-algorithm), local search methods, Simulated Annealing, Genetic Algorithms.
Further Information
Lecturer(s):
Materials:
All learning materials are available in OLAT.
Winter Term
Information Systems
Content
- Information- and knowledge management
- Planning, realization and introduction of application systems
- Analysis and design of internal and intercompany processes
- Fundamentals of IT networking
- Selected areas of business informatics
- Object oriented modelling
- Introduction to Python
Further Information
Lecturer(s):
Materials:
All learning materials are available in Olat.
Artificial Intelligence
Content
Artificial Intelligence I:
- An introductory course to Python
- Array/tensor processing and vectorization
- Basics of parallel programming
Artificial Intelligence II:
- Introduction to Artificial Intelligence
- Linear Models
- Neural Networks
- Intro to Convolutional Neural Networks
- Intro to Reinforcement Learning
Further Information
Lecturer(s):
Materials:
All learning materials are available in Olat.