Learning in Artificial Neural Networks

Related lectures (32)

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Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Introduces systems neuroscience, focusing on neural circuits, memory systems, and course logistics.

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

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

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

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

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

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

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.