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  • Financial Engineering III: Machine Learning

    Zoom link to the lecture here. The lectures are streamed from AS1, Maarintie 8.


    This course starts on Wednesday, January 12, at 14.15. Welcome!

    • The lectures are held every second Wednesday at 14.15-16.00, starting on January 12. (Kaila)
    • The exercise sessions are held every second Wednesday at 14.15-16.00,  starting on January 19. (Toepfer)


    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. 

    Topics to be considered: 

    • Data analysis: Financial data structures, labeling, data weights
    • Modeling: supervised and unsupervised methods (regression, PCA, clustering, random forest, Bayesian methods)
    • Cross-validation: LOOCV, K-Fold
    • Backtesting

    Passing the course: 

    The lectures will be held via 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. 

    3 credit version: 
    Weekly exercises (reflection on the lectures and analysis/solutions of Python exercises done in Jupyter)
    grading: weekly exercises 

    5 credit version: 
    Weekly exercises (reflection on the lectures and analysis/solutions of Python exercises done in Jupyter)
    Assignment (done in group or individually)
    grading: weekly exercises 60 %, assignment 40 %

    6 credit version: 
    same as 5 credit version + essay on a scientific article
    grading: weekly exercises 60 %, assignment 40 %

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

    Schedule of the lectures and exercises
    Wednesday at 14.15-16.00 online (the lectures are streamed from AS3, Maarintie 8)
    changes are possible

    12.1. Lecture 1, online, streamed from AS3

      • Machine learning in finance, introduction
      • Financial data structures


    19.1. Exercise 1


    26.1. Lecture 2, online, streamed from AS3

      • Regression models, Bayesian methods
      • Labeling, Type 1 and type 2 errors, Confusion matrix


    2.2. Exercise 2


    9.2. Lecture 3, online, streamed from AS3

      • Classification models
      • Ensemble methods, Cross-Validation


    16.2. Exercise 3


    2.3. Lecture 4, online, streamed from AS3
      • Clustering, distribution based clustering
      • Feature importance


    9.3. Exercise 4


    16.3. Lecture 5, online, streamed from AS1

      • Dimensionality reduction, Principal component analysis PCA
      • Backtesting, backtest statistics


    23.3. Exercise 5


    30.3. Lecture 6, online, streamed from AS1

      • Natural language processing


    6.4. Exercise 6


    Final deadline for ALL the weekly exercises is 10.4.2022.


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