Indices
- Lectures and Labs (along with readings for these lectures)
- Videos
- Homework
- Topics Index
- Terms Glossary
Sequentially
Week 1
Lecture 1: Introduction
Lecture 2: Probability and Distributions and Frequentism
Lab1: Frequentism
Week 2
Lecture 3: Law of Large Numbers, CLT, and Monte Carlo
Lecture 4: Sampling
Lab2: Python, Math, and Stratification
Week 3
Lecture 5: Machine Learning
Lecture 6: Machine Learning, contd, Gradient Descent
Lab3: PyTorch, Regressions, and Artificial Neural Networks
Week 4
Lecture 7: Machine Learning and Backpropagation
Lecture 8: Neural Nets and Information Theory
Lab4: PyTorch and Artificial Neural Networks contd
Week 5
Lecture 9: Information Theory, Deviance, and Global Optimization
Lecture 10: Annealing, Markov, and Metropolis
Lab5: Simulated Annealing and the TSP
Week 6
Lecture 11: Metropolis To Bayes
Lecture 12: Bayes
Lab 6: Sampling and Pymc3
Lecture 13: More Bayes
Lecture 14: Convergence and Gibbs
Lab7: Sampling and Hierarchicals
Lecture 15: Linear Regression
Lecture 16: Gaussian Processes
Lab8: Regression and GPs
Lecture 17: Augmentation and Slice and HMC
Lecture 18: HMC, Normal Normal Hierarchical
Lab9: Normal-Normal Hierarchicals
Lecture 19: Posterior Predictive Checks and GLMs
Lecture 20: Decisions, Model Comparison, and GLMs
Lab 10: Prosocial Chimps Bernoulli glm
Lecture 21: Decisions, Model Comparison, and GLMs, Ensembles, Workflow
Lecture 22: Workflow and Mixtures
Lab11: Mixtures and log-sum-exp marginals
Lecture 23: EM and Mixtures
Lecture 24: Expectation Maximization and Variational Inference Slides, Notes, and Readings
Lab12: Correlations and Mixtures and ADVI Notes and Material
Lecture 25: Variational Bayes and Generative Models
Lecture 26: Recap