generalization and model complexity
50
regularization
Statistics and Machine Learning
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data
1
height data
2
weight data
hypothesis testing
3
one-sample t-test
4
independent samples t-test
5
statistical power
6
the problem with t-test
7
permutation test
8
numpy vs pandas
9
exact vs. Monte Carlo permutation tests
confidence interval
10
basic concepts
11
analytical confidence interval
12
empirical confidence interval
regression
13
the geometry of regression
14
least squares
15
equivalence
16
partitioning of the sum of squares
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linear mixed effect model
18
logistic regression
19
logistic 2d
correlation
20
correlation
21
correlation and linear regression
22
cosine
23
significance (p-value)
bayes
24
Bayes’ theorem from the ground up
25
parametric generative classification
26
odds and log likelihood
27
logistic connection
28
conjugate prior
29
the boy-girl paradox
30
monty hall
svd and pca
31
SVD for image compression
32
SVD for regression
decision trees
33
CART: classification
34
CART: regression
35
random forest
information theory
36
entropy
37
cross-entropy and KL divergence
likelihood
38
probability and likelihood
39
maximum likelihood estimation
40
MLE and summary statistics
41
MLE and linear regression
42
MLE and information theory
43
MLE and regularization
44
MLE and classification
45
MLE and bayesian inference
generalization and model complexity
46
bias-variance tradeoff
47
overfitting and underfitting
48
cross-validation
49
data splitting
50
regularization
51
when more data changes the tradeoff
52
double descent
miscellaneous
53
trend test
generalization and model complexity
50
regularization
50
regularization
Code
49
data splitting
51
when more data changes the tradeoff