This lecture covers the process of parameter estimation for spike times in neuron models, focusing on quadratic and convex optimization methods, as well as the Generalized Linear Model (GLM) and its concave error function.
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Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.
Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.
