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Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.
Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.
Explores the evolution of image representation, challenges in supervised learning, benefits of self-supervised learning, and recent advancements in SSL.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
