Mathematical Foundations of Data Science
Mathematische Grundlagen der Datenwissenschaft
8 LPWiSeEN
Voraussetzungen
Empfohlen, im Wortlaut
Good command of basic linear algebra, analysis and probability theory is recommended, e.g. as taught in the modules • 11103: Analysis I • 11104: Analysis II • 11101: Lineare Algebra und analytische Geometrie I • 11217: Wahrscheinlichkeitstheorie or 11917: Mathematik W-3 (Statistik) or 11212: Statistics
Inhalte
• Linear Regression • Empirical Risk Minimization • Model Assessment and Model Selection • Bias-Variance Decomposition • Bayesian Decision Theory • Naïve Bayes Classifier • Linear Classifiers
Lernziele
After completing the course, the students are able to apply statistical learning methods to justify conclusions drawn from data. They are familiar with fundamental properties, assumptions, limitations of the considered methods and their derivation.
Prüfungsleistung
Prerequisite: • successful completion of a homework Final module examination: • written exam, 90 min.
Literatur (3)
- Trevor Hastie, Robert Tibshirani, Jerome Friedman: The elements of statistical learning: data mining, inference, and prediction. Springer, 2009.
- Philippe Rigollet, Jan-Christian Hütter: High-Dimensional Statistics, Lecture Notes, MIT, 2023.
- Stefan Richter. Statistical analysis of machine learning algorithms, Lecture Notes, Universität Heidelberg, 2020.
Bemerkungen
• Study programme Mathematical Data Science M.Sc.: Mandatory module • Study programme Mathematics M.Sc.: Compulsory elective module in complex „Stochastics“
Quelle: Modulbeschreibung der BTU · abgerufen 29.09.2026Original