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Fundamentals of Reinforcement Learning
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Explore algorithmic foundations connecting machine learning and control theory through expert lectures from leading researchers in optimization, reinforcement learning, and robotics.
Discover the evolution and future of VR/AR research from Meta's Chief Scientist, covering 11 years of breakthroughs and upcoming innovations in virtual reality technology.
Explore advanced reinforcement learning algorithms that enable robots and language models to learn effectively from minimal data through interactive experience.
Discover how multimodal generative AI transforms precision health by creating digital patient twins from real-world data to optimize care delivery and accelerate biomedical discovery.
Explore programming systems for modern accelerated computing, covering actor-based vs task-based models and automatic optimization strategies for Tensor Core GPUs.
Discover how AI-powered robotic arms can capture, repair, and refuel satellites in orbit, revolutionizing space infrastructure beyond Earth's throwaway culture.
Explore how computational intractability shapes physical explanations, from quantum mechanics interpretations to black hole paradoxes and thermodynamics.
Discover how Nobel laureate David Baker uses deep learning to design new proteins from scratch, addressing modern challenges in medicine, technology, and sustainability.
Explore astral space theory for understanding convex function minimizers at infinity, with extensions of convexity, conjugacy, and subdifferentials concepts.
Explore NASA's VIPER lunar rover mission to map water ice at the Moon's South Pole, featuring extreme environment challenges and autonomous systems design for planetary exploration.
Explore how machine learning reveals brain dynamics, neural causality, and communication between brain regions to understand sensing, thinking, and learning.
Discover how over-parameterized gradient EM algorithms achieve global convergence when learning Gaussian mixture models through advanced theoretical analysis and practical insights.
Discover multi-frequency progressive refinement techniques for solving inverse scattering problems using machine learning methods in computational imaging and signal processing.
Explore data, architecture, and algorithms driving in-context learning through expert insights from University of Michigan researcher in this IFDS workshop.
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