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Background: Pick some application domains of your interest (hobby, research project, master/phd thesis or work-related) and model it as a machine learning problem according to Chapter 2 of my textbook Ref1. In particular, you must define data points, their features and the quantity of interest ("label"). Based on the problem formulation you can apply different ML methods (ranging from plain old linear regression to the latest super-fancy media-hyped deep learning model) and compare their performance in numerical experiments.
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Language requirements: English (oder Deutsch)
Related Work:
Ref1 A. Jung, "Machine Learning: The Basics," Springer, Singapore, 2022 (available electronically at Aalto lib https://primo.aalto.fi/permalink/358AALTO_INST/ha1cg5/alma999673293406526).
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Topic available also for a group: yes
Graded as something else than CS-E4875: no
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Topic #21:
Background: Diagnostics and variance reduction for Bayesian leave-one-out cross-validation (LOO-CV). LOO-CV is a popular approach for assesing and selecting models based on predictive performance. As LOO-CV makes minimal assumptions about the future data distribution it tends o have relaively high variance. Adding some additional weak assumptions we may be able significanlty reduce that variance or gain additional informaion on the trustworthiness given the current data. The project involves short review of the method, implementation of the method in Stan probabilistic programming ecosystem, and making experiments and a case study to illustrate the usefulness of the approach for model assessment and comparison.
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Topic available also for a group: yes
Graded as something else than CS-E4875: no
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