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Guarantees for Machine Learning
Last Updated: 2026-06-01 11:30:51
Abstract
This course is aimed at advanced master and doctorate students who want to conduct independent research on theory for modern machine learning (ML). It teaches standard methods in statistical learning theory commonly used to prove theoretical guarantees for ML algorithms. The knowledge is then applied in independent project work to understand and follow-up on recent theoretical ML results.
Objective
By the end of the semester students should be able to - understand a good fraction of theory papers published in the typical ML venues. For this purpose, students will learn common mathematical techniques from statistical learning in the first part of the course and apply this knowledge in the project work - critically examine recently published work in terms of relevance and find impactful (novel) research problems. This will be an integral part of the project work and involves experimental as well as theoretical questions - outline a possible approach to prove a conjectured theorem by e.g. reducing to more solvable subproblems. This will be practiced in in-person exercises, homeworks and potentially in the final project - effectively communicate and present the problem motivation, new insights and results to a technical audience. This will be primarily learned via the final presentation and report as well as during peer-grading of peer talks.
Content
This course touches upon foundational methods in statistical learning theory aimed at proving theoretical guarantees for machine learning algorithms. It touches on the following topics - concentration bounds - uniform convergence and empirical process theory - regularization for non-parametric statistics (e.g. in RKHS, neural networks) - high-dimensional learning - computational and statistical learnability (information-theoretic, PAC, SQ) - overparameterized models, implicit bias and regularization The project work focuses on current theoretical ML research that aims to understand modern phenomena in machine learning, including but not limited to - how overparameterized models generalize (statistically) and converge (computationally) - complexity measures and approximation theoretic properties of randomly initialized and trained neural networks - generalization of robust learning (adversarial or distribution-shift robustness) - private and fair learning
Resources
Learning Materials (Links)
- Main link
- Information
General Information
- Language
- English
- Levels
- BSC , MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 30
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture |
Guarantees for Machine Learning
The concrete dates when lectures take place will be communicated at the beginning of the semester
|
|
3 h weekly |
| exercise |
Guarantees for Machine Learning
The concrete dates will be communicated at the beginning of the semester
|
|
1 h weekly |
| independent project | Guarantees for Machine Learning | No time listed | 2 h weekly |
Offered In
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Wahlfächer (Für das Master-Diplom in Angewandter Mathematik ist die folgende Zusatzbedingung (nicht in myStudies ersichtlich) zu beachten: Mindestens 14 KP der erforderlichen 26 KP aus Kern- und Wahlfächern müssen aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten stammen.)
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Wahlfächer aus Bereichen der angewandten Mathematik ... (vollständiger Titel: Wahlfächer aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten)
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Vertiefung: Signal Processing and Machine Learning (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
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Vertiefungsfächer (These specialisation courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the MSc EEIT.)
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Statistik Master (Die hier aufgelisteten Lehrveranstaltungen gehören zum Curriculum des Master-Studiengangs Statistik. Die entsprechenden KP gelten nicht als Mobilitäts-KP, auch wenn gewisse Lerneinheiten nicht an der ETH Zürich belegt werden können.)
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