• Lecture 1: Intro and Probability
• Lecture 2: Probability, Distributions, and Frequentism
• lab 1: Frequentism
• Lecture 3: Expectations, the laws, and Monte Carlo
• Lecture 4: Sampling
• lab 2: Python, Math, and Stratification
• Lecture 5: Machine Learning
• Lecture 6: Gradient Descent
• lab 3: PyTorch, Regressions, and Artificial Neural Networks
• Lecture 7: Machine Learning and Backpropagation
• Lecture 8: Neural Nets and Information Theory
• lab 4: PyTorch and Artificial Neural Networks(contd)
• Lecture 9: Information Theory, Deviance, and Global Optimization
• Lecture 10: Annealing, Markov, and Metropolis
• lab 5: Simulated Annealing
• Lecture 11: Metropolis To Bayes
• Lecture 12: Bayes
• lab 6: Sampling and Bayes
• Lecture 13: Bayes
• Lecture 14: Convergence and Gibbs
• lab 7: Sampling and Hierarchical Models
• Lecture 15: Linear Regression
• Lecture 16: Gaussian Processes
• lab 8: Regression and GP
• Lecture 17: Augmentation and Slice and HMC
• Lecture 18: HMC, Normal Normal Hierarchical
• lab 9: Normal-Normal Hierarchicals
• Lecture 19: Posterior Predictive Checks and GLMs
• Lecture 20: Decisions, Model Comparison, and GLMs
• Lab 10: Prosocial Chimps glm
• Lecture 21: Decisions, Model Comparison, and GLMs, Ensembles, Workflow
• Lecture 22: Workflow and Mixtures
• Lab 11: Mixtures and log-sum-exp marginals
• Lecture 23: EM and Mixtures
• Lecture 24: Expectation Maximization and Variational Inference
• Lab 12: Mixtures and Correlation
• Lecture 25: Variational Bayes and Generative Models
• Lecture 26: Recap