CS 533 Fall 2021

  • CS 533 Homepage

Course Info

  • Syllabus
  • Schedule
  • Glossary

Content

  • Week 0 — Pre-Class Welcome
  • Week 1 — Questions (8/23–27)
    • Demo Notebook
    • Data Types and Operations
  • Week 2 — Description (8/30–9/3)
    • Introducing Pandas
    • Aggregates and Groups
    • Describing Distributions
  • Week 3 — Presentation (9/6–10)
    • Drawing Charts
  • Week 4 — Inference (9/13–17)
  • Week 5 — Filling In (9/20–24)
  • Week 6 — Two Variables (Sep. 27–Oct. 1)
  • Week 7 — Getting Data (Oct. 4–8)
  • Week 8 — Regression (Oct. 11–15)
  • Week 9 — Models & Prediction (Oct. 18–22)
  • Week 10 — Classification (10/25–29)
  • Week 11 — More Modeling (11/1–5)
  • Week 12 — Text (11/8–12)
  • Week 13 — Unsupervised (11/15–19)
  • Week 14 — Workflow (11/29–12/3)
  • Week 15 — What Next? (12/6–10)

Assignments

  • Rubric
  • Assignment 0
    • CS 533 Assignment 0
  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Assignment 4
  • Assignment 5
  • Assignment 6
  • Assignment 7

Resources

  • Software and Installation
  • Documentation & Reading
  • Data Sets
  • Notebook Checklist & Guide
  • Notes on Probability
  • Common Problems
  • Remotely Using Onyx
  • Git Resources
  • Software Environments
  • Tutorials
    • Advanced Pipeline Example
    • Tricks with Boolean Series
    • Building Data
    • Finishing Touches
    • Drawing Charts
    • Clustering Example
    • Correlation and Basic Linear Model
    • Charting Movie Scores
    • Distributions
    • Empirical Probabilities
    • Bibliography Fetch
    • Fun with Numbers
    • Writing Functions
    • Indexing
    • Logistic Regression Demo
    • MovieLens Time Series
    • Rebuilding Regression
    • Missing Data
    • Movie Matrix Decomposition
    • One Sample
    • Overfitting Simulation
    • PCA Demo
    • Sampling and Testing the Penguins
    • Regressions
    • Reshaping Data
    • Sampling Distributions
    • SciKit Logistic Regression Demo
    • SciKit Pipeline and Transform Demo
    • SciKit-Learn Linear Regression Demo
    • SciKit Transformations
    • Selecting Data
    • Sessionization
    • Spam Detector Example
    • Tuning Example
    • Using Census Data

Site Details

  • Copyright and License
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Index

A | B | C | D | E | F | G | H | I | J | L | M | n. | N | O | P | R | S | T | U | V

A

  • ablation study
  • aggregate
  • arithmetic mean

B

  • Bayes' theorem
  • Bayesianism
  • bootstrap

C

  • central limit theorem
  • classification
  • conditional probability
  • confidence interval
  • correlation
  • covariance

D

  • degrees of freedom
  • disaggregation

E

  • Elementary Event
  • elementary event
  • embedding
  • encoding
    • dummy
    • one-hot
  • entropy
  • environment variable
  • estimand
  • estimate
  • estimator
  • Euclidean norm
  • Event
  • event
  • expected value

F

  • frequentism

G

  • geometric mean

H

  • HARKing
  • heteroskedasticity
  • homoskedasticity
  • hyperparameter

I

  • inference
  • instance
  • iterative method

J

  • joint probability

L

  • label
  • leakage
  • linear model
  • log odds
  • logistic function
  • logit function
  • L₁ Norm
  • L₂ Norm

M

  • majority-class classifier
  • marginal probability
  • matrix
  • matrix decomposition, [1]
  • mean

n.

  • sample

N

  • naïve Bayes, [1]
  • null hypothesis
  • null hypothesis significance test

O

  • objective function
  • odds
  • odds ratio
  • operationalization
  • overfitting

P

  • p-hacking
  • p-value
  • parameter
  • population
  • prediction

R

  • random variable
  • regression
  • regularization
  • residual

S

  • sample size
  • sampling distribution
  • standard deviation
  • standard error
  • standardization
  • statistic
  • supervision signal
  • supervized learning

T

  • t-test
  • test set
  • training set
  • tuning set

U

  • unbiased estimator
  • unsupervised learning

V

  • validation set
  • variance
  • vector
  • vectorization

By Michael D. Ekstrand
© Copyright 2021.