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Certificates: Design Thinking" certificate
Reinforcement Learning" certificate -
Examination: Practical project work with final presentations
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Teaching Times: Full-timeMonday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
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Language of Instruction: German
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Duration: 5 Weeks
Design Thinking
Introduction to Design Thinking (approx. 1 day)
Design Thinking process at a glance
The most important rules and phases of Design Thinking
Practice-oriented approaches and applications
5 phases in a real project (approx. 3 days)
Research Phase
Methodological input on qualitative research
Implementation through practical exercises on a real project
Synthesis phase
Methodical input on analysis and synthesis
Implementation through practical exercises on a real project
Ideation phase
Methodical input on creative techniques and idea development
Implementation through practical exercises on a real project
Prototyping phase
Methodical input on visualization and prototyping (including mockups, click dummies, 3D printing and rapid prototyping)
Implementation through practical exercises on a real project
Testing phase
Methodical input on test methods and iteration, agile approach
Implementation through practical exercises on a real project
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Project work (approx. 1 day)
To consolidate the content learned
Presentation of the project results
Reinforcement Learning
Introduction to reinforcement learning (approx. 1 day)
Definition and basic concepts
Differences to other learning methods
Areas of application and examples
Markov Decision Processes (MDPs) (approx. 2 days)
Definition and properties of MDPs
Value functions and policy
Bellman equations
Dynamic Programming Approach
Q-Learning (approx. 2 days)
Definition and algorithm
Exploration vs. exploitation
Convergence and optimization properties
Applications in games, robotics and other areas
Deep reinforcement learning (approx. 3 days)
Deep Q-Learning
Deep Deterministic Policy Gradients (DDPG)
Actor Critical Methods
Policy Gradient Methods
Advanced topics (approx. 4 days)
Model-Based Reinforcement Learning
Multi-Agent Reinforcement Learning
Inverse Reinforcement Learning
Meta Reinforcement Learning
Practical applications (approx. 3 days)
Implementation of reinforcement learning algorithms
Application to selected problems and case studies
Evaluation and tuning of the algorithms
Summary and outlook (approx. 2 days)
Summary of the most important concepts and results
Challenges and future developments in reinforcement learning
Project work (approx. 3 days)
To consolidate the content learned
Presentation of the project results
Changes are possible. The course content is updated regularly.
After completing the course, you will understand the basic concepts of reinforcement learning and know the differences to other learning methods. You will be familiar with Markov decision processes, Q-learning and deep reinforcement learning and will be able to apply advanced topics such as multi-agent and model-based reinforcement learning. You will also be able to implement reinforcement learning algorithms, test them on real-world problems and optimize them.
The course also teaches the design thinking approach, which can be used to develop innovative solutions for complex problems. The design thinking approach is clearly structured, iterative and leaves plenty of room for new perspectives. The course conveys the meaning, process and principles of the method.
Computer science, mathematics, electrical engineering and people with a degree in (business) engineering
Reinforcement learning is often used in robotics and automation technology, but also in the automotive industry, e.g. for driver assistance functions, or in the development and optimization of autonomous transport systems. Specialists with the relevant knowledge are in high demand on the job market across all sectors.
Your meaningful certificate provides a detailed insight into the qualifications you have acquired and improves your career prospects.
Didactic concept
Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).
You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.
Virtual classroom alfaview®
Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.
The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).