ep23 - Bassam Bamieh: Sampled Data Systems, PDEs, Distributed Control of Spatially Invariant Systems, Coherence, Resistive Losses, Cochlear Instabilities, and Stochasticity in Feedback Loops
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Bridging Computational Experiments and Theoretical Frameworks in Machine Learning
This chapter explores the vital connection between computational experimentation in machine learning and the necessity of theoretical frameworks to interpret experimental outcomes. By drawing parallels with early 20th-century physics, the speaker emphasizes the importance of developing theories that enhance our understanding of machine learning results, informed by their background in physics and engineering.
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