First and foremost, I share a number of courses which consist of the most sufficiently necessary materials such as reading lists, lecture notes, code & data, assignments (with/without solution) and final exams.
Machine Learning - Deep Learning
Intermdiate Deep Learning class (Fall 2020) - University of Toronto
Statistical Learning Theory and Applications - Fall 2019 - MIT
Deep Reinforcement Learning and Control (Spring 2017) - University of Toronto
Large-Scale Machine Learning (Winter 2015) - University of Toronto
Machine Learning and Adaptive Intelligence 2015 - University of Sheffield
Advanced Machine Learning - Harvard University Comprehensive
Bayesian Statistics
Statistical Modeling with Stochastic Processes - University of British Columbia
A Course in Bayesian Statistics - Stanford University - Winter 2015
Tutorials on Bayesian Nonparametrics Choice Models (Columbia University)
Bayesian Nonparametrics - Princeton UniversityComprehensive
Applied Bayesian Nonparametrics - Brown UniversityComprehensive
Moreover, a number of professors have provided open courses (or workshops, short courses). You can select some methods that are conformable to your research interest.
I am not quite sure about these courses which consist of essential documents like the introduced courses above, because I have not studied them.
Neil Lawrence - University of Sheffield (Family of Gaussian processes and Dimensionality Reduction)Follow
David M. Blei - Columbia University (Hierarchical Dirichlet Processes, Variational Inference)Follow
Erik Sudderth - Dept. of Computer Science - Brown University (Bayesian nonparametric)Follow
Arnaud Doucet - Department of Statistics - Oxford University (MCMC Methods)
John Paisley - Department of Electrical Engineering - Columbia University (Machine learning)