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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, 2021-01-14: first steps in Python, interrogating the UCI dataset
• Lab 2, 2020-01-21: EDA with the UCI dataset
• Lab 3, 2021-01-28: Quetelet and GPAs
• Lab 4, 2021-02-04: Galton
• Lab 5, 2021-02-11: statistics and society
  • Yule and spurious correlations
  • Spearman's g-factor, PCA
  • Simpson's paradox
• Lab 6, 2021-02-18: p-hacking and R. A. Fisher's Statistical Methods for Research Workers, featuring 538's p-hacking
• Lab 7, 2021-02-25: codebreaking at Bletchley: the first data science
• Lab 8, 2021-03-11: perceptrons, AI without ML
• Lab 9, 2021-03-18: early supervised learning from perceptrons to trees
• Lab 10, 2021-03-25: connectionism and ensembles, random forests, neurons, and COVID common task frameworks along with
  • in-lab lecture on trees
  • in-lab lecture on reinforcement learning and the "3 kinds" of machine learning
• Lab 11, 2021-04-01: ethics: justice fairness, disparate impact, disparate treatment, and COMPAS
• Lab 12, 2021-04-08: ethics: rights and privacy