Machine Learning: Unsupervised Learning

Durée : Plus de 6 heures

Catégories :
Niveau des connaissances préalables requises en IA : Aucune connaissance préalable, Connaissances de bases
Niveau des connaissances préalables requises en santé : Aucune connaissance préalable, Connaissances de bases

Langue : Anglais

Accèssibilité : Accès et certification gratuit

Durée : Plus de 6 heures

Sujet : Algorithme (Algorithm), Informatique (Computer science)

描述

This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.
Ever wonder how Netflix can predict what movies you’ll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!
Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how you can use Unsupervised Learning approaches — including randomized optimization, clustering, and feature selection and transformation — to find structure in unlabelled data.

Series Information: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.

The entire series is taught as an engaging dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).

https://www.udacity.com/course/machine-learning-unsupervised-learning–ud741