LEARNING OUTCOMES
This course complements the content of TU-E2210 Financial Engineering I. After the 3 cr. version of the course, the student is able to apply the most common machine learning methods to financial problems and to test the accuracy of the analysis. In addition, the student knows how to prepare financial data for analysis and how to avoid the typical problems related to machine learning in finance.
Additionally, after the 5 cr. course, the student is able to design and complete a small project in machine learning with financial data.
Credits: 6
Schedule: 11.01.2023 - 05.04.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Ruth Kaila, Eljas Toepfer
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
- Data analysis: Financial data structures, labeling, data weights
- Modeling: supervised and unsupervised methods (regression, classification, PCA, clustering, Bayesian methods)
- Cross-validation, backtesting
Assessment Methods and Criteria
valid for whole curriculum period:
weekly exercises and assignment
Workload
valid for whole curriculum period:
lectures, weekly exercises, assignment
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III - IV
2023-2024 Spring III - IV
- Teacher: Kaila Ruth
- Teacher: Toepfer Eljas