Lectures

Given in the Philipps University of Marburg with examples in R.

Details
Science is the belief in the ignorance of experts.
— Richard Feyman, "What is Science?", presented at the fifteenth annual meeting of the National Science Teachers Association, in New York City, 1966.

Scientific approaches should be evaluated very carefully, critically and extensively discussed, as well as tested in real life. On that account, please be most sceptical about anything you read on my website. It serves to propose data science ideas I hold or which I was taught. They may sometimes be hard to prove or contrary to common opinion. Occasionally, they are being published in peer-reviewed journals (see my publications) if I decide that my free time is worth the effort. My focus with regard to data science lies on data visualization, dimensionality reduction, clustering and knowledge discovery, time series analysis, swarm intelligence, self-organization and emergence, game theory and every algorithm derived from effects found in nature. However, improvement of data science methods and approaches is only my hobby. This is the reason why not every idea, which is or will be presented on this website, is going to be published in a peer-reviewed journal. Moreover, not every idea presented here is my own. If you find my ideas worth using please cite me accordingly.

Winter 2019 - Databionic Methods for Artificial Intelligence:

Databionics means the transfer of algorithms for data processing from nature. One major part of databionicsis artificial intelligence (AI). In this context, AI is restricted to seeking, explaining and emulating intelligentbehavior in the form of a computational processes.

Chapter 1: Databionics and Randomness

Chapter 2: Introduction into Supervised Artificial Neural Networks

Chapter 3: Unsupervised Learning Focused on Neural Networks and Emergence

Chapter 4: Behavior-Based Systems

Chapter 5: Evolutionary (EA) and Genetic Algorithms (GA)

Chapter 6: Knowledge Discovery in Genes and Their Products

Summer 2019 - Temporal Data Mining:

Chapter 1:

Chapter 2:

Chapter 3:

Chapter 4:

Chapter 5:

Chapter 6:

Chapter 7:

Chapter 8:

Chapter 9:

Chapter 10:

Supplementary Introduction into R

Following are some of the lectures and talks I gave during my teachings under the tutelage of Prof. A. Ultsch or in conferences. More general lectures on data science:

Cluster Analysis and Visualization of such results:

Methods of Dimensionality Reduction and their Evaluation approaches:

Two brief summaries on knowledge discovery and temporal data mining and basics about high-dimensional data in German: