263-5300-00L  Guarantees for Machine Learning

SemesterFrühjahrssemester 2022
DozierendeF. Yang
Periodizitätjährlich wiederkehrende Veranstaltung
LehrveranstaltungFindet dieses Semester nicht statt.
LehrspracheEnglisch
KommentarNumber of participants limited to 30.

The course will take place next autumn semester 2022.



Lehrveranstaltungen

NummerTitelUmfangDozierende
263-5300-00 GGuarantees for Machine Learning
Findet dieses Semester nicht statt.
3 Std.
Do12:15-14:00CAB G 11 »
Fr12:15-13:00CAB G 59 »
F. Yang
263-5300-00 AGuarantees for Machine Learning
Findet dieses Semester nicht statt.
3 Std.F. Yang

Katalogdaten

KurzbeschreibungThis course is aimed at advanced master and doctorate students who want to conduct independent research on theory for modern machine learning (ML). It teaches classical and recent methods in statistical learning theory commonly used to prove theoretical guarantees for ML algorithms. The knowledge is then applied in independent project work that focuses on understanding modern ML phenomena.
LernzielLearning objectives:

- acquire enough mathematical background to understand a good fraction of theory papers published in the typical ML venues. For this purpose, students will learn common mathematical techniques from statistics and optimization 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 determine impactful (novel) research problems. This will be an integral part of the project work and involves experimental as well as theoretical questions
- find and outline an approach (some subproblem) to prove a conjectured theorem. This will be practiced in lectures / exercise and 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.
InhaltThis course touches upon foundational methods in statistical learning theory aimed at proving theoretical guarantees for machine learning algorithms, touching on the following topics
- concentration bounds
- uniform convergence and empirical process theory
- high-dimensional statistics (e.g. sparsity)
- regularization for non-parametric statistics (e.g. in RKHS, neural networks)
- implicit regularization via gradient descent (e.g. margins, early stopping)
- minimax lower bounds

The project work focuses on current theoretical ML research that aims to understand modern phenomena in machine learning, including but not limited to
- how overparameterization could help generalization ( RKHS, NN )
- how overparameterization could help optimization ( non-convex optimization, loss landscape )
- complexity measures and approximation theoretic properties of randomly initialized and trained NN
- generalization of robust learning ( adversarial robustness, standard and robust error tradeoff, distribution shift)
Voraussetzungen / BesonderesIt’s absolutely necessary for students to have a strong mathematical background (basic real analysis, probability theory, linear algebra) and good knowledge of core concepts in machine learning taught in courses such as “Introduction to Machine Learning”, “Regression”/ “Statistical Modelling”. In addition to these prerequisites, this class requires a high degree of mathematical maturity—including abstract thinking and the ability to understand and write proofs.

Students have usually taken a subset of Fundamentals of Mathematical Statistics, Probabilistic AI, Neural Network Theory, Optimization for Data Science, Advanced ML, Statistical Learning Theory, Probability Theory (D-MATH)

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte7 KP
PrüfendeF. Yang
Formbenotete Semesterleistung
PrüfungsspracheEnglisch
RepetitionRepetition nur nach erneuter Belegung der Lerneinheit möglich.
Zusatzinformation zum Prüfungsmodusone Midterm exam (50%)
Homework (10%)
Course project (40%)

Lernmaterialien

 
HauptlinkInformation
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Gruppen

Keine Informationen zu Gruppen vorhanden.

Einschränkungen

PlätzeMaximal 30
WartelisteBis 06.03.2022

Angeboten in

StudiengangBereichTyp
Data Science MasterWählbare KernfächerWInformation
Doktorat InformatikVertiefung FachwissenWInformation
Elektrotechnik und Informationstechnologie MasterEmpfohlene FächerWInformation
Elektrotechnik und Informationstechnologie MasterVertiefungsfächerWInformation
Informatik MasterWahlfächer der Vertiefung General StudiesWInformation
Informatik MasterWahlfächerWInformation
Informatik MasterWahlfächerWInformation
Informatik MasterErgänzung in Machine LearningWInformation
Informatik MasterErgänzung in Theoretical Computer ScienceWInformation
Mathematik BachelorAuswahl: Weitere GebieteWInformation
Mathematik MasterAuswahl: Weitere GebieteWInformation
Mathematik MasterMachine LearningWInformation
Statistik MasterStatistische und mathematische FächerWInformation
Statistik MasterFachbezogene WahlfächerWInformation