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• Syllabus
• Slack
• Partial timeline
• Student FAQ (grading, logistics)

Lecture Notes

(see Syllabus for overview of course)

• Lecture 1, 2021-01-12: intro to course
• Lecture 2, 2021-01-19: setting the stakes
• Lecture 3, 2021-01-26: risk and social physics
• Lecture 4, 2021-02-02: statecraft and quantitative racism
• Lecture 5, 2021-02-09: intelligence, causality, and policy
• Lecture 6, 2021-02-16: data gets real: mathematical baptism
• Lecture 7, 2021-02-23: WWII, dawn of digital computation
• Lecture 8, 2021-03-09: birth and death of AI
• Lecture 9, 2021-03-16: big data, old school (1958-1980)
• Lecture 11, 2021-03-23: AI2.0
• Lecture 10, 2021-03-30: data science, 1962-2017
• Lecture 12, 2021-04-06: ethics
• Lecture 13, 2021-04-13: present problems: attention economy+VC=dumpsterfire
• Lecture 14, 2021-04-15: future solutions

Lab Notebooks (in Colab)

• Lab 1 python basics; "getting" data
• Lab 2 EDA with data about people (police stats)
• Lab 3 1830s stats; GPAs; BMI; Arbuthnot, 1620-1720's data
• Lab 4 Galton, eugenics
• Lab 5 Yule, Snow, Spearman, Simpson
• Lab 6 p-hacking
• Lab 7 statistical codebreaking
• Lab 8 Shannon, Eliza, AI1.0; perceptron
• Lab 9 Memex; SQL; reidentification; movies
• Lab 10 machine learning
• Lab 11 technical privacies; gerrymandering, see also: [https://www.nytimes.com/interactive/2022/01/27/us/politics/congressional-gerrymandering-redistricting-game-2022.html](2022 NYT game) [http://gametheorytest.com/gerry/game/](another game)
• Lab 12 ethics fairness ; admissions
• Lab 13 lecture on power (little lab participation)