TU-EV - Financial Engineering 3: Data Science and Machine Learning, 21.04.2021-31.05.2021
This course space end date is set to 31.05.2021 Search Courses: TU-EV
Topic outline
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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.
Topics to be considered (at least):
Passing the course:- 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
The lectures will be held via Zoom on Wednesdays at 16.15-18.00
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:
Lecture diary (reflection on the lectures and analysis/solutions of weekly exercises done in Jupyter)
grading: lecture diary
5 credit version:
Lecture diary (reflection on the lectures and analysis/solutions of weekly exercises done in Jupyter)
Assignment (done in group or individually)
grading: lecture diary 60 %, assignment 40 %
6 credit version:
same as 5 credit version + something extra
grading: lecture diary 60 %, assignment 40 %
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.
- Data analysis: Financial data structures, labeling, data weights