STAT 215A ·

This calendar will be updated as the semester progresses.

Week	Date	Content	Assignments	Readings Due (before lecture)
1	Thu 08/24	Lecture 1: Introductions
1	Fri 08/25	Section 0: Git / GitHub + R / Tidyverse	Lab 0: GitHub setup + practice submission Due: 09/02 at 11:59pm
2	Tue 08/29	Lecture 2: Domain question to answer (often about the future), Relevant data collection and cleaning
	Thu 08/31	Lecture 3: Exploratory Data Analysis (EDA)		"The Future of Data Analysis" (Tukey, 1962) [please skim] "Science and Statistics" (Box, 1976) "Veridical data science" (Yu and Kumbier, 2020)
	Fri 09/01	Section 1: Workflow + Rmd + Latex + R Tricks + Lab 1	Lab 1: PECARN data assigned Due: 09/22 at 11:59pm
	Sat 09/02		Lab 0 due 11:59pm
3	Tue 09/05	Lecture 4: PCA: reality check and stability through appropriate data and algorithm perturbations
	Thu 09/07	Guest Lecture by Aaron Kornblith (UCSF) on data collection case study 1 (pediatric ER)
	Fri 09/08	Section 2: Advanced visualization techniques
4	Tue 09/12	Lecture 5: Clustering and prediction problems: reality check and stability through appropriate data and algorithm perturbations.		"Growth in a Time of Debt" (Reinhart & Rogoff, 2010) "Does high public debt consistently stifle economic growth?" (Herndon, Ash, & Pollin, 2014) "Superheat" (Barter & Yu, 2018) Seeing is Believing: 5 Studies about Visual Information Processing
	Thu 09/14	Lecture 6: Clustering and prediction problems (Part 2)		"What's Wrong with Social Science and How to Fix It: Reflections After Reading 2578 Papers"
	Fri 09/15	Section 3: TBA
5	Tue 09/19	Lecture 7: Least Squares (LS). 3-way data split: test set as best proxy for future data. Cross validation.		Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks (Wu et al. 2016) "Funes the Memorious" (Borges, 1942) "Algebraic connectivity of graphs" (Fiedler 1973) "Normalized Cuts and Image Segmentation" (Shi and Malik, 2000)
	Thu 09/21	Lecture 8: Regularized LS: model selection, forward selection, L2boosting
	Fri 09/22	Section 4: TBA	Lab 1 due at 11:59pm Lab 2: linguistic data clustering assigned Due: 10/06 11:59pm
			Lab 1 peer review assigned Due: 09/29 at 11:59pm
6	Tue 09/26	Lecture 9: Lasso and Ridge		"Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist" (Norgeot et al., 2020) "Regression Shrinkage and Selection via the Lasso" (Tibshirani, 1996) "Ridge Regression: Biased Estimation for Nonorthogonal Problems" (Hoerl & Kennard, 1970) John Platt (1964) “Strong inference”, Science Rohe, Qin and Yu (2016) on directed spectral clustering, PNAS Breiman (1995) paper on non-negative garrote, which motivated Lasso Elastic net paper by Zuo and Hastie (2005)
	Thu 09/28	Lecture recording by Stark on data collection regarding election auditing		Garrigues and El Ghaoui (2018) on on-line Lasso Nishimoto et al (2011) on movie reconstruction
	Fri 09/29	Section 5: TBA	Lab 1 peer review due at 11:59pm
7	Tue 10/03	Lecture 10: Lasso and Ridge (part 2)		Notes on Ridge Regression by van Wieringen UCERF3: A New Earthquake Forecast for California’s Complex Fault System Contributions to the mathematical theory of evolution (Pearon 1983) "Remarks on nonparametric estimates of a density function" Rosenblatt (1956)
	Thu 10/05	Lecture 11: Weighted LS. Binary classification through WLS. Prediction with uncertainty measures. Calibration and evaluation or scrutiny of results.		Saltelli et al (2020) on five ways to ensure models serve society
	Fri 10/06	Section 6: Introduce Lab 3	Lab 2 due at 11:59pm Lab 3: computing and evaluating the stability of k-means assigned Due: 10/20 at 11:59pm
8	Mon 10/9		Lab 2 Peer Review assigned Due: 10/16 at 11:59pm
	Tue 10/10	Lecture 12: Sources of randomness. Simple random sampling. Density estimation		Neyman (1923) Rubin (1974) Lin (2013) Freedman book chapter 4: linear regression models
	Thu 10/12	Lecture 13: EM algorithm.		Freedman book chapter 5 Risk Analysis of the Space Shuttle: Pre-Challenger Ali et al (2019) paper on facebook ad process
	Fri 10/13	Section 7: TBA
9			Lab 2 Peer Review due at 11:59pm
	Tue 10/17	Lecture 14: Neyman-Rubin model for A/B testing
	Thu 10/19	Lecture 15: Linear regression, Logistic Regression.
	Fri 10/20	Section 8: TBA
10	Mon 10/23		Lab 3 due at 11:59pm
	Tue 10/24	Mid-term review
	Thu 10/26	Mid-term in class
	Fri 10/27	Section 9: Introduce Lab 4	Lab 4 group project assigned Due: 11/09 at 11:59pm
11	Tue 10/31	Lecture 16: Exponential family.		A Gentle Introduction to Risk-limiting Audits (Lindeman and Stark, 2012) Do-no-harm playbook by Ripley and Kleinfeld (2020) SPRT Derivation (on bCourses)
	Tue 11/02	Lecture 17: GLMS, IRWLS, model checking through calibration		Dobson Ch. 3-4
	Fri 11/03	Section 10: TBA
12	Tue 11/07	Lecture 18: PCS Inference: drawing conclusions from linear regression and logistic regression models through data and al- gorithm perturbations
	Thu 11/09	Lecture 19: Interpretation of data results. Hypothesis testing, sequential testing, and multiple hypothesis testing	Lab 4 due at 11:59pm Final Project assigned Due: 12/10 at 11:59pm	D. F. Freedman and D. Lane (1983) "A Nonstochastic Interpretation of Reported Significance Levels" D. F. Freedman (1995) Some issues in the foundation of statistics
	Fri 11/10	Academic holiday
	Sat 11/11		Lab 4 peer review assigned Due: 11/17 at 11:59pm
13	Tue 11/14	Lecture 20: AIC/BIC, e-L2boosting, Lasso theory; decision trees and random forests
	Thu 11/16	Lecture 21: Advanced topics: iterative random forests, kernel ridge regression, SVMs, adaboost, inter- pretable ML, deep learning		Reread: "Veridical data science" (Yu and Kumbier, 2020) Dwivedi et al. (2020) Stable discovery of interpretable subgroups via calibration in causal studies
	Fri 11/17	Section 11: TBA	Lab 4 peer review due at 11:59pm
14	Tue 11/21	Lecture 22: Advanced topics (Part 2)
	Thu 11/23	Thanksgiving Break
	Fri 11/24	Thanksgiving Break
15	Tue 11/28	Lecture 23: Advanced topics (Part 3)
	Thu 11/30	Lecture 24: PCS revisited and inference
	Fri 12/01	Extra OH
RRR	Fri 12/08		Final Project due at 11:59pm