Lecturer: Benjamin Paaßen
Fields: Machine Learning
Machine learning is concerned with automatically learning models (patterns, regularities, correlations) from known data which generalize to new data. To do so, it combines concepts from mathematics (esp. statistics, probability theory, linear algebra, and optimization), artificial intelligence, and computer science. This course will provide an introduction to machine learning for the un-initiated. Students will need to suffer through some math, but hopefully my enthusiasm will convey the beauty behind it 🙂 And my focus is on lots of examples and pictures.
In more detail, the course will have four sessions with the following topics:
- Basic Concepts: Functions, learning algorithms, optimization, linear regression (as an example of a learning algorithm), regularization, probability theory, machine learning theory, how to design a ML experiment, how to read an ML paper
- Classic machine learning tasks and methods to solve them: The distance perspective on ML, Regression, Classification, Dimensionality Reduction, Clustering
- Artificial neural networks and deep learning: Neural network modules, recipes for neural networks, adversarial attacks
- Reinforcement learning and ethics
Each session is accompanied by a (voluntary) programming exercise in Python. Exercise sheets (and slides) can be found here: https://bpaassen.gitlab.io/Teaching.html
Literature is optional and more regarded as ‘further/complementary reading’:
- Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press. Cambridge, UK. http://www.cs.ucl.ac.uk/staff/d.barber/brml/
- Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. Cambridge, MA, USA. https://www.deeplearningbook.org/
- Paaßen, B. (2019). Lecture Notes on Optimization. Bielefeld University. https://pub.uni-bielefeld.de/record/2935200
- Shalev-Shwartz, S., and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambride University Press. Cambridge, UK. https://www.cs.huji.ac.il/w~shais/UnderstandingMachineLearning/
Benjamin Paaßen received their doctoral degree in intelligent systems in 2019 from Bielefeld University on the topic of ‘Metric Learning for Structured Data’. Afterwards, they received a DFG research fellowship for a stay at The University of Sydney in Australia and Humboldt-University of Berlin. Since 2021, they are deputy head of the educational technology lab at the German Research Center for Artificial Intelligence (DFKI). Their research foci are machine learning on structured data and artificial intelligence for education.
Affiliation: German Research Center for Artificial Intelligence (DFKI)