Lecture

Nearest Neighbor Rules: Part 2

In course

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Description

This lecture covers the Nearest Neighbor Rules, focusing on the k-NN algorithm and its challenges in high-dimensional spaces. It explains the Bayes classifier solution and the k-means algorithm for clustering, with examples and applications to the MNIST dataset.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_xz48vpyb

About this result

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Ontological neighbourhood

Information engineering

Machine learning: Topics in machine learning, Artificial neural networks, Unsupervised learning

Data science: Dimensionality reduction

Mathematics

Algebra: Linear algebra

Mathematical logic: Set theory

Geometry: Euclidean geometry

Statistics

Statistical inference: Mathematical statistics

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