OUR DATA SCIENCE WORKSHOPS

Crash Course Artificial Intelligence

This course is suitable for the qualification of employees and managers, as well as doctoral students and students who want to expand their knowledge around the topic of artificial intelligence. After the course, the participants are able to classify first use cases themselves and have developed an understanding of the basic concepts of AI.

Participants of the course will receive a certificate afterwards with a detailed breakdown of the contents learned. For larger numbers of participants, for multi-day or customized workshops, prices are available upon request.

Duration: 2 hours

Group of participants: Up to 20 participants, no previous knowledge required

Procedure: Remote / on-site at your location / on-site at DataLab Aachen

Costs: 1.500€

Module 1: Introduction to the basic concepts of Machine Learning: Supervised & Unsupervised Learning

Using practical and real examples from the corporate world, the basic concepts of machine learning are introduced here
Module can either be done purely conceptually or with more in-depth examples & concepts, time must be allocated accordingly for this

Module 2: Introduction Basic Concepts Neural Networks

What is a neural network, what makes it better than other algorithms, what are the use cases?
Module can either be done purely conceptually or with more in-depth examples & concepts, time must be allocated accordingly for this

Artificial intelligence for decision makers

This course is suitable for the qualification of decision makers, innovators and executives who want to expand their knowledge around the topic of artificial intelligence and its influence in business. After the course, participants will be able to classify initial use cases themselves and will have developed an understanding of the basic concepts of AI. Furthermore, they will be able to estimate the effort of projects and assign them to their departments.

Duration: 4 hours

Group of participants: Up to 20 participants, no previous knowledge required

Procedure: Remote / on-site at your location / on-site at DataLab Aachen

Costs: 1.900€

Module 1: Introduction to the basic concepts of Machine Learning: Supervised & Unsupervised Learning

Using practical and real examples from the corporate world, the basic concepts of machine learning are introduced here
Module can either be done purely conceptually or with more in-depth examples & concepts, time must be allocated accordingly for this

Module 2: Introduction Basic Concepts Neural Networks

What is a neural network, what makes it better than other algorithms, what are the use cases?
Module can either be done purely conceptually or with more in-depth examples & concepts, time must be allocated accordingly for this

Module 3: Procedure in Data Science Projects

How do you ensure that a data science project is a success instead of wasting resources for months?
What are the basic concepts behind data-driven projects and how are they translated into concrete steps?

Module 4: Important complementary concepts

Having introduced the basic concepts, here is where we complete the picture. What is the first step in ensuring that a machine learning model works as well as intended in live operation? Is more data always better? Why do nutrition studies keep coming up with wildly different results, and how do I prevent the same thing from happening in my business?

Artificial intelligence - introduction, possibilities and limits

Duration: 8 hours

Group of participants: Up to 20 participants, no previous knowledge required

Procedure: Remote / on-site at your location / on-site at DataLab Aachen

Costs: 2.900€

Module 1: Introduction to the basic concepts of Machine Learning: Supervised & Unsupervised Learning

Using practical and real examples from the corporate world, the basic concepts of machine learning are introduced here
Module can either be done purely conceptually or with more in-depth examples & concepts, time must be allocated accordingly for this

Module 2: Introduction Basic Concepts Neural Networks

What is a neural network, what makes it better than other algorithms, what are the use cases?
Module can either be done purely conceptually or with more in-depth examples & concepts, time must be allocated accordingly for this

Module 3: Procedure in Data Science Projects

How do you ensure that a data science project is a success instead of wasting resources for months?
What are the basic concepts behind data-driven projects and how are they translated into concrete steps?

Module 4: Important complementary concepts

Having introduced the basic concepts, here is where we complete the picture. What is the first step in ensuring that a machine learning model works as well as intended in live operation? Is more data always better? Why do nutrition studies keep coming up with wildly different results, and how do I prevent the same thing from happening in my business?

Module 5: History and Outlook Artificial Intelligence

Looking at the future of AI in the next 5, 15, 50 years, there are many different views and speculations. However, if we look at the history of AI over the last few decades, it is possible to make a well-founded forecast of what developments can be expected.

Module 6: Ethics in AI

To shed light on the complexities of ethics in AI, it is not enough to talk about autonomous weapon systems. Ethical implications of ongoing automation or even the influence of poorly evaluated machine learning models in experiments are often much more important in everyday life. Those who want to hand over some of their decisions to AI should keep these layers in mind.

Module 7: Practical Examples & Programming Neural Networks

Those who already have basic experience in programming but do not yet know how to create a neural network in Python, or those who simply want to know how the concepts discussed look in reality even without programming experience, can follow along here and experiment interactively themselves while concrete examples are explained line by line.