Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Probabilistic Machine Learning
Introduction
Essential Info and Links
A Generative Refresher on Probability and Bayes
Primer on Probability
Probability and Computation
Assessment: Computational Probability and Monte Carlo
Data, populations, statistics, and models
Case Study: Revisiting Bayes via Training Machine Learning Algorithms
Case Study: Revisiting Bayes via Machine Learning In Production
Food for thought: Probabilistic Reasoning Systems
Food for Thought: Models as Symbolic Explanation Generators for Data
Directed Graphical Models
Building a model with a directed graphical model
Reasoning about Probability with the DAG
Assessment: Modeling with DAGs
Inference on a DAG
Assessment: Inference on a DAG
Inference Algorithms
Stochastic simulation and Bayesian computation
Assessment - Stochastic Simulation
Primer on Hamiltonian Monte Carlo
Primer on Approximate Inference
Stochastic Variational Inference
Probabilistic Programming
Intro to Probabilistic Programming
Execution, Sampling, Conditioning
Guide Functions for Variational Inference
Bayesian Programming
Patterns for Bayesian Programming
Cheat sheet for Selecting Priors
Visual Model Diagnostics
Monte Carlo Reasoning on the Posterior
Predictive Checks
Guided Project Part 1: Bayesian Regression
Shannon's Communication Theory and Information Criteria
Shannon's Model of Communication (5:44)
The Map is Not the Territory: Separating the Receiver and Transmitter (5:29)
Transmitter-Receiver Case Study (4:09)
Information Theory for Model Evaluation and Selection
Bayesian Cross Validation
Making domain knowledge consistent with the model assumptions
Getting close to the source of data
Guided Project Part 2: Transmitter-Receiver Modeling, Bayesian CV, and Model Comparison
Hierarchical and Latent Variable Models
Hierarchy and Heterogeneity: A Case Study
Mixed Model, Random Effects and the Notorious 8 Schools
Partial Pooling
Exchangeability, de Finetti, and Causality
Thinking Causally about Hierarchy
Common Classes of Latent Variable Models
Guided Project Part 3: Latent Variables and Bayesian Regression
Bonus: Bayesian Decision Theory
Bayesian Risk: Connect Data to Decisions
Bayesian Decision Theory
Modeling actions as causal interventions
Decision Rules for Choosing Actions
Statistical Hypothesis Testing, Bayes Rules, and Admissibility
Bayesian Thompson Sampling
Making domain knowledge consistent with the model assumptions
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock