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Instructors: Thomas Heil; Prof. Dr. Franziska Julia Peter
Event type:
Seminar / exercise
Org-unit: Corporate Management & Economics
Displayed in timetable as:
Advanced Methods | Y
Hours per week:
1,5
Credits:
3,0
Location:
Campus der Zeppelin Universität
Language of instruction:
Englisch
Min. | Max. participants:
10 | 24
Priority scheme: Standard-Priorisierung
Course content:
Advanced Methods | Y | Machine Learning primarily focuses on the rapidly emerging field of machine learning. Therefore, the course aims to provide students with advanced knowledge in quantitative methods. Whereas classical quantitative methods specialize in recreating reality by employing predefined models, machine learning offers the ability to let machines (computers) learn from the data. The introduction to these methods should give the students the necessary knowledge to further study the field of machine learning and the ability to apply the course content to their own research projects.
Part 1: Introduction: What is Learning?
- What is Artificial Intelligence?
- What is Machine Learning?
- What is Statistical Learning?
- What is learning from data?
Part 2: Some introductory Methods for Regression
- Least Squares and Subset Selection
- Shrinkage Methods for Subset Selection (Ridge and LASSO)
Part 3: Some introductory Methods for Classification
- Introduction to classification
- Logistic Regression (briefly)
- K-Nearest Neighbors
Part 4: Simple Decision Trees and how to improve their Predictive Power
- Decision Trees
- Random Forests
(Part 5: Neural Networks)
- Single Hidden Layer
- Multilayer Perceptron
Requirements:
Basic knowledge in linear algebra, stochastics, geometry
BBasic knowledge in econometrics, especially linear regression, OLS, logistic regression
Further information about the exams:
Assessment: Take - Home Assignment
Mandatory literature:
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)
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