Machine Learning (SS 2026)

Machine Learning

 

In this course we will introduce the mathematical 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 lecture is based on the previous machine learning courses offered by Matthias Hein and Peter Ochs.

The tentative list of topics cover:

• 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

 

Prerequisites: The course is targeted to students in computer science, bioinformatics, math, and related areas with a mathematical background. Students should know linear algebra and have good basic knowledge of statistics, for example by having taken Mathematics for Computer Scientists I and II (for linear algebra) and Statistics Lab or Mathematics for Computer Scientists III (for statistics). In addition, prior attendance to machine learning related courses, e.g., Elements of Machine Learning, is considered as additional useful background.

 

Organizational Information

Lectures:

• Mondays at 14:15h  -- Building E2.2, Günter-Hotz Lecture hall I (0.01) 
• Thursdays at 12:15h -- Building E2.2, Günter-Hotz Lecture hall I (0.01) 

Lectures will start on April 13th! 

Tutorials (to pick between):  

• Mondays at 12:15h -- Building E1.3, Room HS002
• Mondays at 16:15h -- Building E1.3, Room HS001
• Tuesdays at 10:15h -- Building E1.3, Room HS001
  • 05.05.26 and 07.07.26 at Building E2.2, Room GHH
• Tuesdays at 12:15h -- Building E1.3, Room HS002
• Wednesdays at 14:15h -- Building E1.3, Room HS002
  • 06.05.26 at Building E1.3, Room HS001
• Wednesdays at 16:15h -- Building E1.3, Room HS002

Tutorials will start on April 20th!

Exams: 

• Mid-term (admission) exam: 21.05.2026
• Main exam: 04.08.2026
• Re-exam: 28.09.2026

 

Tentative Schedule:

	Week	Date	Lecture Nr.	Title
1. Introduction to ML	1	13-Apr	1	Introduction
	1	16-Apr	2	Bayesian Decision Theory
	2	20-Apr	3	Empirical Risk Minimization
	2	23-Apr		No Class
2. Linear Supervised ML	3	27-Apr	4	Linear Regression I
	3	30-Apr	5	Linear Regression II
	4	4-May	6	Linear Classification
	4	7-May	7	Performance Measures I
	5	11-May	8	Performance Measures II
	5	14-May	Holiday	No Class
	6	18-May		Introduction to Project
	6	21-May		Mid-term Exam
	7	25-May	Holiday	No Class
3. Unsupervised Learning	7	28-May	9	Clustering
	8	1-Jun	10	Dimensionality Reduction
	8	4-Jun	Holiday	No class
4. SVM and kernel methods	9	8-Jun	11	Convex Optimization I
	9	11-Jun	12	Convex Optimization II
	10	15-Jun	13	Linear SVM
	10	18-Jun	14	Intro to Kernels
	11	22-Jun	15	Learning with Kernels
	11	25-Jun		No Class / Maybe Q&A
5. Deep learning	12	29-Jun	16	Deep Learning I - Feedforward Nets + BP
	12	2-Jul	17	Deep Learning II - CNNs
	13	6-Jul	18	Deep Learning III - Transformers
	13	9-Jul	19	Beyond Supervised DL, and Q&A
6. Societal impact	14	13-Jul	20	Societal Impact
	14	16-Jul	21	Explainability & Interpretability

 

Bibliography

[Bishop] Bishop, C. M. Pattern recognition and machine learning. Springer, 2006

[DSH] Duda, R. O., Hart, P.E., and Stork, D.G. Pattern classification (2nd edition). Wiley-Interscience 2000

[Boyd]   Boyd, S., Boyd, S. P., and Vandenberghe, L. . Convex optimization. Cambridge university press, 2004

[eML]    Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning. Springer, 2009.

[Kernels] Smola, A. J.,  & Schölkopf, B. Learning with kernels. GMD-Forschungszentrum Informationstechnik, 1998.

[DL]   Goodfellow, I., Courville, A., and Bengio, Y. Deep learning. MIT press, 2016.

[DL]   Simon J.D. Prince Understanding Deep Learning. MIT press, 2023.

[DL]   Bishop, C. M. & Bishop, H Deep Learning: Foundations and Concepts. Springer, 2024.