|
Instructors: Prof. Dr. Franziska Julia Peter
Event type:
Seminar and lecture
Org-unit: Corporate Management & Economics
Displayed in timetable as:
Quan. M II
Hours per week:
3
Credits:
6,0
Note: In your exam regulations, differing credits may have been specified.
Location:
Campus der Zeppelin Universität
Language of instruction:
Englisch
Min. | Max. participants:
10 | 24
Priority scheme: Standard-Priorisierung
Course content:
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.
The first part of the course consists of lectures and practical applications using Python (no previous knowledge with Python is required)
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.
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)
Assessment: Take - Home Assignment + Presentation
Wenn Sie E-Learning Funktionalitäten nutzen möchten, tragen Sie bitte "Ja" ein.:
Ja
|
