Unsupervised learning

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Related lectures (30)

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Improving Models of the Ventral Visual Pathway

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Introduction to Machine Learning

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Different types of learning

Covers supervised, unsupervised, and reinforcement learning in neurorobotics.

Optimization of Paper Planes

Explores the optimization of paper planes and soft structures for thrust generation.

Image Representation Insights

Explores the evolution of image representation, challenges in supervised learning, benefits of self-supervised learning, and recent advancements in SSL.

Machine Learning Basics

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Machine Learning at the Atomic Scale

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Support Vector Machine Extensions: SVM, RVM, Transductive SVM

Explores SVM extensions, RVM, Transductive SVM, and support vector clustering in advanced machine learning.