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Certificates: Design Thinking" certificate
Machine Learning" certificate
Reinforcement Learning" certificate -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
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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: 9 Weeks
Design Thinking
Introduction to Design Thinking (approx. 1.5 days)
Design Thinking process at a glance
The most important rules and phases of Design Thinking
Practice-oriented approaches and applications
Digital tools and AI in the innovation process
Phase 1: Research (approx. 0.5 days)
Methods of user-centered research
Interview techniques and needs analysis
AI-supported research and information processing
Phase 2: Synthesis (approx. 0.5 days)
Analysis and structuring of findings
Development of problem definitions and personas
Visualization and clustering of results
Phase 3: Ideation (approx. 0.5 days)
Creative techniques for developing ideas
Methods for finding and evaluating solutions
Use of generative AI in the ideation process
Phase 4: Prototyping (approx. 0.5 days)
Development of initial solution approaches and mockups
Introduction to rapid prototyping and click dummies
Digital tools for the visualization of concepts
Phase 5: Testing (approx. 0.5 days)
Methods for carrying out tests and feedback rounds
Analysis and optimization of solution approaches
Iterative work and agile further development
Project work (approx. 1 day)
To consolidate the content learned
Presentation of the project results
Machine Learning
Introduction to Machine Learning (approx. 5 days)
Why machine learning?
Application examples
Supervised learning, unsupervised learning, partially supervised learning, reinforcement learning
Examples of data sets
Getting to know data
Training, validation and test data
Viewing data
Making predictions
Supervised learning (approx. 5 days)
Classification and regression
Generalization, overfitting and underfitting
Size of the data set
Algorithms for supervised learning
Linear models
Bayes classifiers
Decision trees
Random Forest
Gradient Boosting
k-nearest neighbors
Support Vector Machines
Conditional Random Field
Neural Networks and Deep Learning
Probabilities
Unsupervised learning (approx. 5 days)
Types of unsupervised learning
Preprocessing and scaling
Data transformations
Scaling training and test data
Dimension reduction
Feature engineering
Manifold learning
Principal component decomposition (PCA)
Non-negative matrix factorization (NMF)
Manifold learning with t-SNE
Cluster analysis
k-Means clustering
Agglomerative clustering
Hierarchical cluster analysis
DBSCAN
Cluster algorithms
Evaluation and improvement (approx. 2 days)
Model selection and model evaluation
Tuning the hyperparameters of an estimator
Cross-validation
Grid search
Evaluation metrics
Classification
Project work (approx. 3 days)
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 the course, you will have relevant knowledge of machine learning. You will know the most important reasons for using machine learning, areas of application and the various categories and concepts of machine learning. You will round off your knowledge with skills in evaluation and improvement.
You will also understand the basic concepts of reinforcement learning and know the differences to other learning methods. You are familiar with Markov decision processes, Q-learning and deep reinforcement learning and can apply advanced topics such as multi-agent and model-based reinforcement learning.
The course also teaches the design thinking approach, which can be used to develop innovative and user-centered solutions for complex challenges. You will learn about the principles and the structured, iterative process of design thinking and find out how practice-oriented tools, digital tools and artificial intelligence support creative and interdisciplinary innovation processes.
Computer science, mathematics, electrical engineering and people with a degree in (business) engineering.
Machine learning is used in numerous areas of application: the independent development of suitable spam filters for the internet, the creation of precise forecasts of stock levels in supply chain management or the development of purchase forecasts for individual customers or customer segments in marketing. Employees who are qualified in the field of machine learning can be deployed across all industries and are therefore in high demand on the job market.
With reinforcement learning, you will also gain cross-industry knowledge that 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.
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).