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Artificial intelligence (AI) has developed rapidly in recent years and offers numerous opportunities for optimizing business processes and developing new business models. However, the variety of application areas and the complexity of the technology often make it difficult to maintain an overview and make informed decisions.
This course is aimed specifically at decision-makers who are looking for a comprehensive but easy-to-understand introduction to the world of artificial intelligence. Basic concepts are taught and practical application examples and various use cases from different industries are discussed. The focus is on how to plan AI projects efficiently, assess their feasibility and realistically calculate typical resource and time requirements.
The course provides a practical overview of current AI technologies and their possible applications. It also explains that there is no universal approach to AI projects and how the resources and time required can vary considerably depending on the use case.
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Course Contents
-
- What is AI?
- Introduction to machine learning, data science and deep learning
- Possibilities and limitations - opportunities and challenges
- Market overview - important platforms, services and tools at a glance
- Use cases and application examples
- Data security, ethics
The detailed digital documentation package, consisting of an e-book and PDF, is included in the price of the course.
-
Target Group
-
This course is aimed at anyone who wants a broad overview of artificial intelligence and wants to be able to make decisions for AI projects.
-
Knowledge Prerequisites
-
No prior knowledge is required for this course. It is recommended that you bring your own questions and challenges from your day-to-day work in order to promote a practical exchange.
| 1 | Introduction to Artificial Intelligence |
| 1.1 | What is artificial intelligence? |
| 1.2 | History of AI |
| 1.3 | Classical Programming and AI |
| 1.3.1 | The Potential of AI |
| 1.3.2 | Explainability of AI |
| 1.4 | Machine Learning |
| 1.4.1 | Models |
| 1.4.2 | Supervised Learning |
| 1.4.3 | Unsupervised Learning |
| 1.4.4 | Reinforcement Learning |
| 1.5 | Predictive vs Generative AI |
| 1.5.1 | Multimodality |
| 1.5.2 | Language Models |
| 1.6 | Excursus: Chatbot Training |
| 1.6.1 | Level 1: Pre-training |
| 1.6.2 | Level 2: Supervised Fine-tuning |
| 1.6.3 | Level 3 & 4 Reinforcement Learning |
| 2 | Application of AI |
| 2.1 | Classification |
| 2.2 | Regression |
| 2.3 | Cluster Analysis |
| 2.4 | Dimension Reduction |
| 2.5 | Anomaly Detection |
| 2.6 | Image Generation |
| 2.7 | Video Generation |
| 2.8 | Chatbots |
| 2.8.1 | Text Generation & Translation |
| 2.8.2 | Formulation of Texts |
| 2.8.3 | Brainstorming |
| 2.8.4 | Programming |
| 2.8.5 | Github Copilot |
| 2.9 | Tool Usage & Agents |
| 2.9.1 | Search Engines |
| 2.9.2 | Retrieval-Augmented Generation (RAG) |
| 2.10 | Copilot in Microsoft Products |
| 3 | Implementation of AI Projects |
| 3.1 | Platforms for AI |
| 3.1.1 | Hugging Face |
| 3.1.2 | Kaggle |
| 3.2 | Using Models Locally |
| 3.3 | Hardware as a driver for AI innovation? |
| 3.4 | Large Language Models |
| 3.4.1 | Training Data for LLMs |
| 3.4.2 | Training and Inference: Hardware |
| 3.4.3 | Benchmarks for Performance Comparison |
| 3.4.4 | Attacks |
| 3.5 | Small Language Models |
| 3.5.1 | Quantization |
| 3.5.2 | Monitoring |
| 3.6 | Model and Data Drift |
| 4 | Regulations, Ethics, and Working Environment |
| 4.1 | Technological Progress |
| 4.2 | Regulations in Europe |
| 4.2.1 | Digital Markets Act |
| 4.3 | Regulations in America |
| 4.3.1 | Licenses & Terms of Use |
| 4.4 | Data Protection and Privacy |
| 4.5 | Ethics and Social Justice |
| 4.6 | Changes of the Working Environment |
| 4.6.1 | Awareness Movements |
| 4.6.2 | Robots |
| 4.6.3 | Overview of AI Trends |
| 4.7 | Artificial General Intelligence |
| 4.7.1 | Ambiguity of the Term |
| A | List of Abbreviations |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.-
Artificial intelligence (AI) has developed rapidly in recent years and offers numerous opportunities for optimizing business processes and developing new business models. However, the variety of application areas and the complexity of the technology often make it difficult to maintain an overview and make informed decisions.
This course is aimed specifically at decision-makers who are looking for a comprehensive but easy-to-understand introduction to the world of artificial intelligence. Basic concepts are taught and practical application examples and various use cases from different industries are discussed. The focus is on how to plan AI projects efficiently, assess their feasibility and realistically calculate typical resource and time requirements.
The course provides a practical overview of current AI technologies and their possible applications. It also explains that there is no universal approach to AI projects and how the resources and time required can vary considerably depending on the use case.
-
Course Contents
-
- What is AI?
- Introduction to machine learning, data science and deep learning
- Possibilities and limitations - opportunities and challenges
- Market overview - important platforms, services and tools at a glance
- Use cases and application examples
- Data security, ethics
The detailed digital documentation package, consisting of an e-book and PDF, is included in the price of the course.
-
Target Group
-
This course is aimed at anyone who wants a broad overview of artificial intelligence and wants to be able to make decisions for AI projects.
-
Knowledge Prerequisites
-
No prior knowledge is required for this course. It is recommended that you bring your own questions and challenges from your day-to-day work in order to promote a practical exchange.
| 1 | Introduction to Artificial Intelligence |
| 1.1 | What is artificial intelligence? |
| 1.2 | History of AI |
| 1.3 | Classical Programming and AI |
| 1.3.1 | The Potential of AI |
| 1.3.2 | Explainability of AI |
| 1.4 | Machine Learning |
| 1.4.1 | Models |
| 1.4.2 | Supervised Learning |
| 1.4.3 | Unsupervised Learning |
| 1.4.4 | Reinforcement Learning |
| 1.5 | Predictive vs Generative AI |
| 1.5.1 | Multimodality |
| 1.5.2 | Language Models |
| 1.6 | Excursus: Chatbot Training |
| 1.6.1 | Level 1: Pre-training |
| 1.6.2 | Level 2: Supervised Fine-tuning |
| 1.6.3 | Level 3 & 4 Reinforcement Learning |
| 2 | Application of AI |
| 2.1 | Classification |
| 2.2 | Regression |
| 2.3 | Cluster Analysis |
| 2.4 | Dimension Reduction |
| 2.5 | Anomaly Detection |
| 2.6 | Image Generation |
| 2.7 | Video Generation |
| 2.8 | Chatbots |
| 2.8.1 | Text Generation & Translation |
| 2.8.2 | Formulation of Texts |
| 2.8.3 | Brainstorming |
| 2.8.4 | Programming |
| 2.8.5 | Github Copilot |
| 2.9 | Tool Usage & Agents |
| 2.9.1 | Search Engines |
| 2.9.2 | Retrieval-Augmented Generation (RAG) |
| 2.10 | Copilot in Microsoft Products |
| 3 | Implementation of AI Projects |
| 3.1 | Platforms for AI |
| 3.1.1 | Hugging Face |
| 3.1.2 | Kaggle |
| 3.2 | Using Models Locally |
| 3.3 | Hardware as a driver for AI innovation? |
| 3.4 | Large Language Models |
| 3.4.1 | Training Data for LLMs |
| 3.4.2 | Training and Inference: Hardware |
| 3.4.3 | Benchmarks for Performance Comparison |
| 3.4.4 | Attacks |
| 3.5 | Small Language Models |
| 3.5.1 | Quantization |
| 3.5.2 | Monitoring |
| 3.6 | Model and Data Drift |
| 4 | Regulations, Ethics, and Working Environment |
| 4.1 | Technological Progress |
| 4.2 | Regulations in Europe |
| 4.2.1 | Digital Markets Act |
| 4.3 | Regulations in America |
| 4.3.1 | Licenses & Terms of Use |
| 4.4 | Data Protection and Privacy |
| 4.5 | Ethics and Social Justice |
| 4.6 | Changes of the Working Environment |
| 4.6.1 | Awareness Movements |
| 4.6.2 | Robots |
| 4.6.3 | Overview of AI Trends |
| 4.7 | Artificial General Intelligence |
| 4.7.1 | Ambiguity of the Term |
| A | List of Abbreviations |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.