Neuroscience and Machine Learning: Understanding Visual Intelligence

Related lectures (31)

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Examines the transformative impact of AI on engineering disciplines and the associated challenges.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Explores dynamical modeling, decoding, and control of brain network activity for personalized therapy.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

Explores the reconstruction of a full-scale mouse striatal cellular level model to integrate and interpret striatal data.

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

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

Explores the history and challenges of biologically inspired computation, focusing on building intelligent machines and understanding brain function.