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chris wiggins edited this page May 6, 2020
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(see Syllabus for overview of course)
- Lecture 1, 2020-01-21: intro to course
- Lecture 2, 2020-01-28: setting the stakes
- Lecture 3, 2020-02-04: risk and social physics
- Lecture 4, 2019-02-12: statecraft and quantitative racism
- Lecture 5, 2019-02-19: intelligence, causality, and policy
- Lecture 6, 2019-02-26: data gets real: mathematical baptism
- Lecture 7, 2019-03-05: WWII, dawn of digital computation
- Lecture 8, 2019-03-12: birth and death of AI
- Lecture 9, 2019-03-26: big data, old school
- Lecture 10, 2019-04-02: data science, 1962-2017
- Lecture 11, 2019-04-09: AI2.0
- Lecture 12, 2019-04-16: ethics
- Lecture 13, 2019-04-23: present problems: attention economy+VC=dumpsterfire
- Lecture 14, 2019-04-30: future solutions
- Lab 1, 2020-01-23: first steps in Python, interrogating the UCI dataset
- Lab 2, 2020-01-30: EDA with the UCI dataset
- Lab 3, 2020-02-06: Quetelet and GPAs
- Lab 4, 2020-02-13: Galton
- Lab 5, 2020-02-20: statistics and society
- Yule and spurious correlations
- Spearman's g-factor, PCA
- Simpson's paradox
- Lab 6, 2020-02-27: p-hacking and R. A. Fisher's Statistical Methods for Research Workers, featuring 538's p-hacking
- Lab 7, 2020-03-05: codebreaking at Bletchley: the first data science
- Lab 8, 2020-03-12: eliminated owing to class cancellations
- Lab 9, 2020-03-28: early supervised learning from perceptrons to trees
- Lab 10, 2020-04-04: connectionism and ensembles, random forests, neurons, and COVID common task frameworks along with in-lab lecture on trees
- Lab 11, 2020-04-11: ethics: justice fairness, disparate impact, disparate treatment, and COMPAS
- Lab 12, 2020-04-18: ethics: rights and privacy
- Lab 13, 2020-04-25: problems, transparency and thinkfluencing
- Lab 14, 2020-05-02: solutions, the future, including data in the time of COVID