Neumann G. Machine Learning - Foundations and Algorithms 2025

Textbook in PDF format

Algorithms that can improve their performance using training data
Typically we have a large number of parameters
Learned from data
Useful if:
No expert knowledge available: industrial/manufacturing control, mass spectrometer analysis, drug design, astronomic discovery
Black-box expert knowledge: face/handwriting/speech recognition, driving a car, flying a plane
Fast changing phenomena: credit scoring, financial modeling, diagnosis, fraud detection
Customization/personalization: personalized news reader, movie/book recommendation
Why do we see this “explosion” now?
More training data
More computation power
New algorithms (deep learning, generative models)…
– But: same principles as from 40 years ago are still in use
"when you go from 10,000 training examples to 10 billion training examples, it all starts to work. Data trumps everything.”