Machine Learning Fundamentals

Related lectures (32)

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Delves into the continual learning of representational models in brain intelligence, emphasizing rapid adaptation to unstructured environments.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

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

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.