Machine Learning

18.03.2025: Webpage is online.

Description

In this course we will introduce the foundations of machine learning (ML). In particular, we will focus on understanding the theoretical aspects of ML that have made ML successful in a wide range of applications such as bioinformatics, computer vision, information retrieval, computer linguistics, robotics, etc.

The course gives a broad introduction into machine learning methods from a theoretical point of view. After the lecture the students should be able to solve and analyze learning problems. 

The tentative list of topics covers:

• Probability theory
• Maximum Likelihood/Maximum A Posteriori Estimators
• Bayesian decision theory
• Linear classification and regression
• Model selection and evaluation
• Convex Optimization
• Kernel methods
• Societal Impact of Machine Learning
• Unsupervised learning (Clustering, Dimensionality Reduction)
• Introduction to Deep Learning

Registration

The coure will be organized via CMS.

Literature

The lecture is based on the following literature.

• Bach, F.: Learning Theory from First Principles. Lecture Notes, available online, 2024.
• Bishop, C. M.: Pattern recognition and machine learning. Springer, 2006
• Duda, R. O., Hart, P.E., and Stork, D.G.: Pattern classification (2nd edition). Wiley-Interscience 2000
• Boyd, S. P., and Vandenberghe, L.: ;Convex optimization. Cambridge university press, 2004
• Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning. Springer, 2009.
• Smola, A. J., : &ichölkopf, B.: Learning with kernels. GMD-Forschungszentrum Informationstechnik, 1998.
• Goodfellow, I., Courville, A., and Bengio, Y.: Deep learning. MIT press, 2016.
• Simon J.D. Prince: Understanding Deep Learning, MIT press, 2023.