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Lehrende: Thomas Heil
Veranstaltungsart:
Seminar
Orga-Einheit: Corporate Management & Economics
Anzeige im Stundenplan:
Quan. M II
Semesterwochenstunden:
3
Credits:
6,0
Hinweis: In Ihrer Prüfungsordnung können abweichende Credits festgelegt sein.
Standort:
Campus der Zeppelin Universität
Unterrichtssprache:
Englisch
Min. | Max. Teilnehmerzahl:
10 | 35
Prioritätsschema: Standard-Priorisierung
Inhalte:
This course aims to provide students with basic and advanced knowledge in quantitative methods. Quantitative Methods II primarily focuses on the rapidly emerging field of statistical learning. Whereas classical quantitative methods specialize in recreating reality by employing predefined models, statistical learning offers the ability to let machines (computers) learn from data. The advocated methods should give the students the ability to apply the course content to own research projects.
Part 1 : Introduction: The Problem of Learning
- What is statistical learning?
- What is learning from data?
- Training vs Test sets
- Optimization
- Supervised vs. Unsupervised learning
Part 2: Linear Methods for Regression
- Least Squares
- K-Nearest-Neighbors
- Shrinkage Methods
Part 3: Linear Methods for Classification
- Introduction to classification and probability modeling
- Logistic regression
(Part 4: Nonparametric Methods)
- Kernels
- Polynomials
- Splines
Part 5: Model Selection
- Bias-Variance trade-off
- Information Criteria
- Cross-Validation
Part 6: Semi-Parametric Models
- Generalized Additive Models
- Trees
Part 7: Neural Networks
- Single Hidden Layer
- Multilayer Perceptron
- Recurrent Neural Networks
Requirements:
- Basic knowledge in linear algebra (especially matrix algebra), stochastics, geometry.
- Basic knowledge in econometrics, especially linear regression, OLS, Maximum Likelihood.
If you are unfamiliar with the requirements, please use the provided literature.
Weitere Informationen zu den Prüfungsleistungen:
The course grading is split into a mid-term exam and a paper presentation at the end of the course.
Literatur:
Hastie T., Tibshirani S., Friedman J., The Elements of Statistical Learning, Springer Series in Statistics, Second Edition (2009)
Abu-Mostafa Y., Magdon-Ismail M., Lin HT., Learning From Data, AMLBook, 2012
James G., Witten D., Hastie T., Tibshirani S., Introduction to Statistical Learning, Springer Texts in Statistics, First Edition (2013)
Preliminary Literature:
Wooldridge J., Introductory Econometrics: A Modern Approach, Mason, Ohio :South-Western Cengage Learning, 2012.
Sydsaeter K., Hammond P., Essential Mathematics for Economic Analysis, Pearson, 2016 (German Edition available)
Wenn Sie E-Learning Funktionalitäten nutzen möchten, tragen Sie bitte "Ja" ein.:
Ja
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