CS 533 Fall 2021
CS 533 Homepage
Course Info
Syllabus
Schedule
Glossary
Content
Week 0 β Pre-Class Welcome
Week 1 β Questions (8/23β27)
Demo Notebook
Data Types and Operations
Week 2 β Description (8/30β9/3)
Introducing Pandas
Aggregates and Groups
Describing Distributions
Week 3 β Presentation (9/6β10)
Drawing Charts
Week 4 β Inference (9/13β17)
Week 5 β Filling In (9/20β24)
Week 6 β Two Variables (Sep. 27βOct. 1)
Week 7 β Getting Data (Oct. 4β8)
Week 8 β Regression (Oct. 11β15)
Week 9 β Models & Prediction (Oct. 18β22)
Week 10 β Classification (10/25β29)
Week 11 β More Modeling (11/1β5)
Week 12 β Text (11/8β12)
Week 13 β Unsupervised (11/15β19)
Week 14 β Workflow (11/29β12/3)
Week 15 β What Next? (12/6β10)
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
repository
open issue
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
Lβ Norm
Lβ Norm
M
majority-class classifier
marginal probability
matrix
matrix decomposition
,
[1]
mean
n.
sample
N
naΓ―ve 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