Lecturer: Benjamin Paaßen
Fields: Machine Learning
Content
Machine learning is concerned with automatically learning models (patterns, regularities, correlations) from training data such that these models generalize to new data. To do so, machine learning 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 I will employ ample examples and pictures.
In more detail, the course will have four sessions with the following topics:
1. 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
2. Classic machine learning tasks and methods to solve them: The distance perspective on ML, Regression, Classification, Dimensionality Reduction, Clustering
3. Artificial neural networks and deep learning: Neural network modules, recipes for neural networks, including current LLMs, adversarial attacks
4. Reinforcement learning and fairness
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
- 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/
- Biehl, M. (2023). The Shallow and the Deep: A biased introduction to neural networks and old school machine learning. University of Groningen Press. Groningen, Netherlands. https://doi.org/10.21827/648c59c1a467e
- 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
Lecturer
Benjamin Paaßen is Junior Professor for Knowledge Representation and Machine Learning at Bielefeld University, Germany. Their focus is on domain-informed, interpretable, and explainable machine learning, with a particular focus on machine learning for education. They are also affiliate researcher at the Educational Technology Lab of the German Research Center for Artificial Intelligence (DFKI), member of the Young College of the Northrhine-Westphalian Academy of Sciences and Arts, and Junior Fellow of the German Computer Science Society.
Affiliation: Bielefeld University
Homepage: https://bpaassen.gitlab.io/