Probabilistic Models and Machine Learning · Statistics
all sorts of things related to bayesian modeling - various models, various inference techniques. overall, a very thorough and well organized experience.
Good overview of statistical methods. Very broad class that touches on a variety of topics.
Many familiar models (Gaussian Mixture Models, Generalized Linear Models, VAEs, etc.) in the framework of probabilistic machine learning.
some basics in probabilistic models and machine learning, including lasso, ridge, GLM, trees, clustering. The class also covers some practical issues like validation and interpretation.
ML and neural networks but from a Bayesian perspective
this was a very engaging course in statistics ML, the most useful part was the practical advice on how to do proper model training
I learned principles of graphical models, their application and background.
different models (LDA, exp famility, GLM, NN, etc) and inference methods (MAP, Gibbs sampling, VI, BBVI etc)
Different type of probabilistic models and inference algorithms
A learned a lot: different statistical models and when to apply them regarding the structure of data. Inference algorithms that allow for inference of complex models. Also, state of the art topics on Statistics that bridge deep learning with probabilistic modeling.