Learning Estimation & Control: the human loss function скачать в хорошем качестве

Learning Estimation & Control: the human loss function

The course introduces modern techniques in mathematical analysis of biomedical data. Techniques include maximum likelihood, estimation theory via Kalman equation, application of Kalman to Brain-Machine Interfaces, state-space models, Bayesian estimation, classification of labeled data, dimensionality reduction, clustering, expectation maximization, and dynamic programming and reinforcement learning via the Bellman equation. Lecture 4. This lecture shows how to use the second derivative of the loss function to guide the process of learning. We then focus on the question of how to estimate the loss function of a learner: is minimizing the squared error a reasonable estimate of how the brain learns, or is there a better loss function to approximate learning in the brain?