Construct and apply sophisticated Bayesian models for complex machine learning tasks. This course covers advanced inference techniques like Markov Chain Monte Carlo (MCMC) and Variational Inference (VI), probabilistic graphical models, Gaussian Processes, and Bayesian deep learning. Emphasis is placed on theoretical understanding coupled with practical implementation for AI engineers seeking to integrate probabilistic approaches and uncertainty quantification into their workflows.
Prerequisites Strong ML, Probability, Python
Level:
Advanced Inference Implementation
Implement and diagnose complex MCMC algorithms (Metropolis-Hastings, Gibbs, HMC) and Variational Inference methods (CAVI, SVI).
Sophisticated Bayesian Modeling
Formulate, build, and critique advanced Bayesian models including Gaussian Processes, Probabilistic Graphical Models, and Bayesian Neural Networks.
Uncertainty Quantification
Accurately represent and interpret model uncertainty using Bayesian approaches.
Model Selection and Comparison
Apply advanced techniques for comparing Bayesian models and evaluating their performance.
Scalable Bayesian Methods
Understand and apply techniques for scaling Bayesian inference to larger datasets.