• Lecture 1: Intro and Probability
• Lab 1: Bayes Theorem and Python Tech
• Lecture 2: Probability and LLN
• Lecture 3: From Monte-Carlo to frequentism
• Lab 2: Frequentism, Bootstrap, and MLE
• Lecture 4: MLE, Sampling, and Learning
• Lecture 5: Regression, AIC, Info. Theory
• Lab 3: Generating regression data, fitting it, training, and testing
• Lecture 6: Risk and Information
• Lecture 7: From Entropy to Bayes
• Lab 4: Bayesian Quantities in the Globe Model
• Lecture 8: Bayes and Sampling
• Lecture 9: Bayes and Sampling
• Lab 5: Logistic Regression and Sundry Bayesian
• Lecture 10: Sampling and Gradient Descent
• Lab 6: Sampling and PyTorch
• Lecture 11: Gradient Descent and Neural Networks
• Lecture 12: Non Linear Approximation to Classification
• [Lab 7] still to come
• Lecture 13: Classification, Mixtures, and EM
• Lecture 14: EM and Hierarchcal models
• Lab 8: EM and Hierarchicals
• Lecture 15: MCMC
• Lecture 16: MCMC and Gibbs
• Lab 9: Sampling and pymc3
• Lecture 17: Data Augmentation, Gibbs, and HMC
• Lecture 18: HMC, and Formal tests
• Lab 10: Jacobians and Tumors
• Lecture 19: NUTS, Formal tests, and Hierarchicals
• Lecture 20: Regression, GLMs, and model specification
• Lab 11: Gelman Schools Hierarchical and Prosocial Chimps GLM
• Lecture 21: From Hierarchical GLMs to Gaussian Processes
• Lecture 22: Decisions and Model Comparison
• Lecture 23: Cross-Validation, Priors, and Workflow
• Lab 12: GLM and Workflow
• Lecture 24: Variational Inference
• Lecture 25: Variational inference and Mixtures
• Lecture 26: Wrapup