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chris wiggins edited this page May 2, 2019
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(see Syllabus for overview of course)
- Lecture 1, 2019-01-22: intro to course
- Lecture 2, 2019-01-29: setting the stakes
- Lecture 3, 2019-02-05: 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 (1958-1980)
- 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: introduction to the notebook, data provenance
- Lab 2: Exploratory Data Analysis; Slides
- Lab 3
- Lab 3, Part 1: Quetelet
- Lab 3, Part 2: EDA continued
- Lab 4
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Lab 4, Part 1: Life expectancy
- Life expectancy data
- Lab 4, Part 2: Simulating Galton
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Lab 4, Part 1: Life expectancy
- Lab 5
- Lab 5, Part 1: Yule, multiple regression and social thought
- Lab 5, Part 2: PCA
- Lab 6: Gould + IQ, T-tests, p-value hacking
- Lab 7: Bayesian Cryptography
- Lab 8
- Lab 8, part 1: Eliza and Expert Systems
- Lab 8, part 2: The birth, death, and rebirth of the Perceptron
- Lab 9 not in 2018: MCMC vs models, perceptron and supervised-v-unsupervised learning
- Lab 10
- Lab 10, part 1 Machine learning: predictive/supervised, prescriptive/reinforcement, descriptive/unsupervised (notebook authored by Su Hang)
- Slides on "interpretability" and the history of forests and trees
- Lab 10, part 2: forests and trees
- Lab 11 Databases and recommendation engines
- Lab 12
- Lab 12, part 1: Privacy
- Lab 12, part 2: Fairness, accountability, and transparency
- Lab 13 readings on autonomy and identity