| Week | Date | Tuesday | Thursday | Due |
|---|---|---|---|---|
| 1 | 01/11 | Ch 1Statistical inference paradigms (frequentist Β· Bayesian Β· likelihood) |
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| 2 | 01/18 | Ch 2Sampling from distributions: the inverse-CDF method |
π Homework 1Preliminaries: Appendices + Ch 1β° Due 1/22 |
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| 3 | 01/25 | Ch 2Rejection sampling |
Ch 2Variance reduction methods |
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| 4 | 02/01 | Ch 3Exponential families |
Ch 3Maximum likelihood: likelihood, score & Fisher information |
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| 5 | 02/08 | Ch 3Maximum likelihood: computing MLEs & asymptotic theory |
Ch 3Sampling variability; the delta method |
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| 6 | 02/15 | Ch 3Linear models: OLS, GaussβMarkov & inference |
Ch 3OLS diagnostics; leverage & influence |
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| 7 | 02/22 | Ch 3GLMs: framework, link functions & logistic regression |
Ch 3GLMs: Poisson & gamma regression; IRLS & diagnostics |
π Homework 2Chapters 2 & 3β° Due 2/26 |
| 8 | 03/01 |
π MIDTERM I
In Class Β· Chapters 1β3
|
Ch 4The sampling-distribution problem; ECDF & the plug-in principle |
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| 9 | 03/08 | Ch 4The nonparametric bootstrap; bootstrap standard errors & CIs |
Ch 4The parametric bootstrap |
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| 10 | 03/15 |
|
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| 11 | 03/22 | Ch 4Bootstrap hypothesis tests & permutation tests |
Ch 5Bayesian foundations; the Bayesian update |
π Homework 3Chapter 4β° Due 3/26 |
| 12 | 03/29 | Ch 5Prior distributions; conjugacy & analytic posteriors |
Ch 5Credible intervals (ETI Β· HDI Β· ROPE) |
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| 13 | 04/05 | Ch 5Markov chains; MCMC foundations |
Ch 5MetropolisβHastings & Gibbs sampling |
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| 14 | 04/12 | Ch 5PyMC; convergence diagnostics |
Ch 5Model comparison (WAIC / LOO); posterior predictive checks |
π Homework 4Chapter 5β° Due 4/16 |
| 15 | 04/19 |
π MIDTERM II
In Class Β· Chapters 4β5
|
Ch 6LLM foundations; embeddings & feature extraction |
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| 16 (Quiet Week) | 04/26 | Ch 6Text preprocessing & annotation; retrieval-augmented generation |
Ch 6Prompt engineering & tool use; reliability & responsible AI |
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| 17 (Finals Week) | 05/03 | π€ CAPSTONE PRESENTATIONS Date TBA | ||