Schedule and Syllabus

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