Topic outline

  • Machine Learning in Financial Engineering


    The first lecture is on Wednesday, January 10, at 14.15-16.00 in TU7, Maarintie 8 and in zoom. Welcome!


    The Zoom link is here.


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    IMPORTANT: prerequisite for the course: 

    TU-E2210 Financial Engineering I + Python/R

    OR 

    basics in finance + Python/R.

    We will use Python during the course, and the algorithms will be given only in Python. A student who is very strong in R can use it. The course will be calibrated to the level of the students of Financial Engineering I. 

    We work with financial data and  consider the following topics: 

    • Data analysis: Financial data structures, labeling, data weights
    • Modeling: supervised and unsupervised methods (regression, classification, clustering, PCA, Bayesian methods)
    • Cross-validation
    • Backtesting
    • Natural Language Processing

    Passing the course: 

    6 lectures and 6 exercise sessions​

    • Lectures every second Wednesday at 14-15-16.00, Ruth Kaila, starting 10.1.2024​

    • Exercises every second Wednesday at 14-15-16.00, Eljas Toepfer, starting 17.1.2024​

    The lectures will be held in a hybrid form in TU5/AS6 and in Zoom on Wednesdays at 14.15-16.00
    The exercises will be done individually in Jupyter.

    A 3 credit version, a 5 credit version or a 6 credit version of the course are available. 

    •  Biweekly work sheets, the fundamental exercises (reflection on the lectures and analysis/solutions of Python exercises done in Jupyter)​

    grading: passed/failed​


    5 credit version: ​

    •  Biweekly work sheets, fundamental exercises (reflection on the lectures and analysis/solutions of Python exercises done in Jupyter)​

    • Biweekly work sheets, independent exercises (finding your own data and working with this)​

    grading: passed/failed​


    6 credit version: ​


    • same as 5 credit version + essay on a scientific article​
    grading: passed/failed

    ​
    This course can be included in the minor Financial Engineering. Click here for further information.


    For additional information, please contact the FE III team: Ruth Kaila, ruth.kailaATaalto.fi or Eljas Toepfer, eljas.toepferATaalto.fi.