An Introduction Into Modern Artifical Intelligence
Successful artificial intelligence ( AI ) projects are constantly being advertised and there are many companies offering AI technologies and services. These technologies are supposed to help to open up new business areas or to achieve cost reductions. However, the offers regularly hide the weighty difference between artificial ability and artificial intelligence. In addition, high expectations or false objectives often lead to lucrative opportunities remaining untapped.
The lecture will illustrate the prerequisites for the exploitation of artificial intelligence based on data as well as its limitations. Knowledge of a specific programming language is not required. Of course, questions and comments can be asked during and after the lecture via the chat function.
What is the difference between artificial ability and AI?
- AI: Explanation of terms and connections to Data Science and Knowledge Discovery.
- Examples are used to illustrate how AI approaches work and what they are used for if they are limited to artificial skills.
- On the basis of a concrete prototype, it is made clear when the use of true artificial intelligence becomes necessary.
- Which algorithms are transferable to the industrial environment?
Duration: 1 hour + QA
Speaker: Prof. Dr. Alfred Ultsch
Target Audience: All people with interesting AI projects
Registration: To register for this free event, please use the form below. After registration we will contact you with details by Email.
Knowledge Discovery: A vocational training in 5 days
The Knowledge Discovery course is the practical application of Data Science to extract useful and usable, and most importantly, understandable knowledge from a collection of data.
Day 1: Data Science in practice
- Building Data Science projects, from Ambition to Data Availability & Quality to Analytics and Embedding
Knowledge as the goal of a feasibility study in data mining
- Non-linearity of the approach, use of insights in the workflow of knowledge discovery
- Basic explorative analyses already provide first insights e.g. about the determination of distribution and cumulative distribution
Day 2: Data becomes tangible – visualization of high-dimensional data
- Distances and correlations
- Projection methods
- Methods of dimension reduction
Day 3: Search for groupings based on similarity
- Recognition of density and distance based structures
- Grouping/Clustering Methods
- Structure discovery machine learning
- Identification of natural high dimensional structures with Human in the Loop.
Day 4: Skill-based AI for automated prediction and diagnosis
- Subsymbolic classifiers
- Artificial Neural Networks
- Extraction of explanations for structures
Day 5: Knowledge-based AI
- Symbolic methods (e.g., decision trees, rippers)
- Practical extraction of understandable and usable knowledge from data, i.e.
Understandability, knowledge representation, and estimation calculi (e.g., fuzzy).
- Embedding of data science projects
- Typical challenges for AI prototyping.
- Objective of automation vs. human-machine communication
Price: 3.950,- € (including related
documentation and exam fee)
Next Dates: 20.09.2021
Duration: 5 days / 8h a day
Speakers: Prof. Dr. Alfred Ultsch
Dr. Michael Thrun
Certification: The training will be concluded with a
written exam. All passing participants will receive a
certification, attesting theire competence in working
with state of the art AI technology.
Target Audience: Professionals wanting to develop their AI competency
Prerequisites: Fundamental IT Skills, ideally some experience with programming,
statistics or data handling.
Registration: Please fill out the registration form below. The course is limited
to 10 participants on a first come first served basis. We reserve the right to
cancel the course in the event of too few participants.
If you are interested in this course, please also consider participating in the free Seminar above (An Introduction Into Modern Artifical Intelligence).