This article is the index for STA414 and related machine-learning materials on Undertide. It collects the generated course-note map and supporting articles on ELBO, EM, variational inference, Bayesian regression, neural networks, and exam planning.
Core Notes
STA414 Machine Learning II MOC
The generated map of content for probability foundations, Bayesian inference, exponential families, latent variable models, EM, variational inference, graphical models, sampling, and neural networks.
Exam and Aidsheet Material
STA414 Final Aidsheet Strategy
A final aidsheet layout strategy organized by retrieval value rather than chapter order.
STA414: Variational Inference, ELBO, EM, and Mixture Models
A problem-style sheet on VI, ELBO, EM, and Gaussian mixture models.
STA414 Simulation 2 - Bayesian Linear Regression and Gaussian Processes
Problem set material connecting Bayesian linear regression and Gaussian processes.
Variational Inference and ELBO
Mean Field Questions and Variational Inference Variations
Problem sets on mean-field VI, coordinate ascent updates, EM vs VI, and diagonal Gaussian encoders.
EM, ELBO, and a Bernoulli-Gaussian Mixture
A worked exercise showing responsibilities, ELBO decomposition, and M-step updates.
Machine Learning: Variational Inference and ELBO
An earlier narrative explanation of variational inference and ELBO.
Machine Learning Foundations
Bayesian Linear Regression Lifecycle
A worked Bayesian linear regression example covering posterior derivation, MAP, and predictive uncertainty.
Neural Network Lifecycle
A worked neural-network example covering forward pass, backpropagation, and one update step.
Machine Learning: Concepts and Knowledges
A rough holding page for machine-learning concepts and knowledge fragments.
机器学习核心概念速记：从似然到 MCMC
中文机器学习核心概念速记，覆盖 likelihood、VAE、DDPM、attention 和 MCMC。