| W1 |
Jan 22 |
Introduction and Probability |
Basic Statistics |
Frequentist Stats Full example |
HW1 |
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| W2 |
Jan 29 |
Sampling and Monte Carlo |
Sampling |
Python, Math, and Stratification |
HW2 |
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| W3 |
Feb 5 |
Machine Learning |
Machine Learning, Gradient Descent |
PyTorch |
HW3 |
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| W4 |
Feb 12 |
Machine Learning and BackPropagation |
Neural Nets and Information Theory |
PyTorch and ANN |
HW4 |
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| W5 |
Feb 19 |
Information Theory, Deviance, Global Optimization |
Annealing, Markov, and Metropolis |
TSP and Sudoku |
HW5 |
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| W6 |
Feb 26 |
Metropolis to Bayes |
Bayes |
Sampling and Pymc3 |
HW6 |
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| W7 |
Mar 5 |
More Bayesian |
Convergence and Gibbs |
Sampling and Hierarchicals |
HW7 |
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| WH |
Mar 12 |
SPRING BREAK |
SPRING BREAK |
SPRING BREAK |
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| W8 |
Mar 19 |
Recap, Linear Regression |
Gaussian Processes |
Linear and GP regression |
HW8: |
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| W9 |
Mar 26 |
Data Augmentation and HMC |
Exploring HMC, HMC tuning, NUTS |
Gelman Schools lab: problems with hierarchicals. |
HW9: |
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| W10 |
Apr 2 |
Model Checking and GLMs |
Model Comparison and Selection (non glm and glm) |
Prosocial Chimps GLM with model comparison. |
HW10: |
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| W11 |
Apr 9 |
More model comparison, Hidden variables and mixture models. Semi-supervized learning and Expectation Maximization. |
Mixtures, Expectation Maximization contd and correlations |
Marginalizing over discretes and mixture model sampling issues. |
HW11: |
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| W12 |
Apr 16 |
Correlation modelling and Variational Inference |
Variational Inference and ADVI |
Correlations, Mixtures and ADVI |
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| W13 |
Apr 23 |
Variational Inference and Advanced Topics |
READING PERIOD, Advanced Topics |
READING PERIOD, Conclusion and Philosophy |
Paper Due Friday |
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| W14 |
Apr 30 |
READING PERIOD |
EXAM PERIOD |
EXAM PERIOD |
None |
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| W15 |
May 7 |
EXAM PERIOD |
EXAM PERIOD |
Exam Due Friday |
Exam Due Friday |
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