Home

Resources

(see Syllabus for overview of course)

Lab 1, 2019-01-24: first steps in Python, interrogating the UCI dataset
Lab 2, 2019-01-31: EDA with the UCI dataset
Lab 3, 2019-02-07: Quetelet and GPAs
Lab 4, 2019-02-14: Galton
Lab 5, 2019-02-21: statistics and society
- Yule and spurious correlations
- Spearman's g-factor, PCA
- Simpson's paradox
- PCA with social data
Lab 6, 2019-02-28: p-hacking and R. A. Fisher's Statistical Methods for Research Workers, fearing 538's p-hacking
Lab 7, 2019-03-07: codebreaking at Bletchley: the first data science
Lab 8, 2019-03-14: AI 1.0
- Expert systems
- Perceptron
Lab 9, 2019-03-28: databases and recommendation systems featuring the Netflix Prize story
Lab 10, 2019-04-04: trees, along with in-lab lecture on trees
Lab 11, 2019-04-11:
- interactive: 3 types of machine learning (unsupervised, supervised, and reinforcement learning) along with in-lab lecture
- Text data
- Fairness, accountability, transparency, part I disparate impact, disparate treatment, and COMPAS
Lab 12, 2019-04-18: normative and technical approaches to defining and defending privacy
- our own database of ruin: constructing and de-identifying
- Fairness, accountability, transparency, part II featuring ToS/EULAs
Lab 13, 2019-04-25: problems
Lab 14, 2019-05-02: solutions