Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


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: 3 - 6

Schedule: 10.01.2024 - 10.04.2024

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


  • 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

  • valid for whole curriculum period:

    lectures, weekly exercises, assignment


Study Material
  • valid for whole curriculum period:

    will be given during the course

Substitutes for Courses


Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Spring III - IV
    2023-2024 Spring III - IV