Lecture

Data Representation: BoW and Imbalanced Data

Description

This lecture covers the concepts of overfitting vs underfitting, model selection, validation set method, LOOCV, k-fold cross-validation, penalizing overfitting, regularized linear regression, kernel ridge regression, and finding the right regularization strength. It also discusses data representation, data normalization, missing data, noisy data, cleaning methods, and the challenges of imbalanced data in machine learning.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_q3o2f7as

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Ontological neighbourhood

Statistics

Data analysis: Cross-validation

Data collection: Statistical sampling

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