Basics of Statistical Learning
Welcome
0.1
Who?
0.1.1
Readers
0.1.2
Author
0.1.3
Acknowledgements
0.2
What?
0.3
Why?
0.4
Where?
0.5
When?
0.6
How?
0.6.1
Build Tools
0.6.2
Active Development
0.6.3
License
1
Machine Learning Overview
1.1
What is Machine Learning?
1.2
Machine Learning Tasks
1.2.1
Supervised Learning
1.2.2
Unsupervised Learning
1.3
Open Questions
1.4
Source
2
Linear Regression
2.1
Reading
2.2
Explanation versus Prediction
2.3
Setup
2.4
Mathematical Setup
2.5
Linear Regression Models
2.6
Using
lm()
2.7
The
predict()
Function
2.8
Data Splitting
2.9
Regression Metrics
2.9.1
Graphical Evaluation
2.10
Example: “Simple” Simulated Data
2.11
Example: Diamonds Data
2.12
Example: Credit Card Data
2.13
Source
3
Nonparametric Regression
3.1
Reading
3.2
Mathematical Setup
3.3
k-Nearest Neighbors
3.4
Decision Trees
3.5
Example: Credit Card Data
3.6
Source
4
Bias–Variance Tradeoff
4.1
Reducible and Irreducible Error
4.2
Bias-Variance Decomposition
4.3
Simulation
4.4
Estimating Expected Prediction Error
4.5
Source
5
Regression Overview
5.1
Goal
5.2
Strategy
5.3
Models
5.4
Model Flexibility
5.5
Overfitting
5.6
Bias-Variance Tradeoff
5.7
Source
6
Classification
6.1
Reading
6.2
Classification Metrics
6.3
Source
7
Nonparametric Classification
7.1
Reading
7.2
Source
8
Logistic Regression
8.1
Reading
8.2
Source
9
Binary Classification
9.1
Reading
9.2
Source
10
Generative Models
10.1
Reading
10.2
Linear Discriminant Analysis
10.3
Quadratic Discriminant Analysis
10.4
Naive Bayes
10.5
Discrete Inputs
10.6
Source
11
Supervised Learning Overview I
11.1
Reading
11.2
Source
12
Simulation
12.1
Reading
12.2
Source
13
Bootstrap Resampling
13.1
Reading
13.2
Source
14
Cross-Validation
14.1
Reading
14.2
Source
15
Supervised Learning Overview II
15.1
Classification
15.1.1
Tuning
15.2
Regression
15.2.1
Methods
15.3
External Links
15.4
rmarkdown
16
Regularization
16.1
Reading
16.2
Source
17
Dimension Reduction
17.1
Reading
17.2
Source
18
Ensemble Methods
18.1
Reading
18.2
Source
Appendix
A
Additional Reading
A.1
Books
A.2
Papers
A.3
Blog Posts
A.4
Miscellaneous
B
Ten Simple Rules for Success in STAT 432
B.1
Rule 1: There Are No Rules
B.2
Rule 2: Read the Syllabus
B.3
Rule 3: Previous Learning is Not Gospel
B.4
Rule 4: All Statements Are True
B.5
Rule 5: Don’t Miss The Forest For The Trees
B.6
Rule 6: You Will Struggle
B.7
Rule 7: Keep It Simple
B.8
Rule 8: RTFM
B.9
Rule 9: There Are No Stupid Questions
B.10
Rule 10: Learn By Doing
B.11
Conclusion
B.12
Source
C
Computing
C.1
Reading
C.2
Additional Resources
C.2.1
R
C.2.2
RStudio
C.2.3
R Markdown
C.3
STAT 432 Idioms
C.3.1
Don’t Restore Old Workspaces
C.3.2
R Versions
C.3.3
Code Style
C.3.4
Reference Style
C.3.5
STAT 432 R Style Overrides
C.3.6
STAT 432 R Markdown Style
C.3.7
Style Heuristics
C.3.8
Objects and Functions
C.3.9
Print versus Return
C.3.10
Help
C.3.11
Keyboard Shortcuts
C.3.12
Common Issues
C.4
Source
D
Probability
D.1
Reading
D.2
Probability Models
D.3
Probability Axioms
D.4
Probability Rules
D.5
Random Variables
D.5.1
Distributions
D.5.2
Discrete Random Variables
D.5.3
Continuous Random Variables
D.5.4
Distributions in R
D.5.5
Several Random Variables
D.6
Expectations
D.7
Likelihood
D.8
References
D.8.1
Videos
D.9
Source
E
Statistics
E.1
Reading
E.2
Statistics
E.3
Estimators
E.3.1
Properties
E.3.2
Example: MSE of an Estimator
E.3.3
Estimation Methods
E.3.4
Maximum Likelihood Estimation
E.3.5
Method of Moments
E.3.6
Empirical Distribution Function
E.4
Source
References
© 2020 David Dalpiaz
Basics of Statistical Learning
Chapter 13
Bootstrap Resampling
In this chapter…
13.1
Reading
Required:
???
library
(
"tidyverse"
)
TBD
13.2
Source
R
Markdown:
bootstrap.Rmd