likelihood
46
MLE and bayesian inference
Statistics and Machine Learning
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data
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height data
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weight data
hypothesis testing
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one-sample t-test
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independent samples t-test
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statistical power
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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
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the geometry of regression
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least squares
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partitioning of the sum of squares
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R-squared
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equivalence
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linear mixed effect model
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logistic regression
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logistic 2d
correlation
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correlation
22
correlation and linear regression
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cosine
24
significance (p-value)
bayes
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Bayes’ theorem from the ground up
26
parametric generative classification
27
odds and log likelihood
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logistic connection
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conjugate prior
30
the boy-girl paradox
31
monty hall
svd and pca
32
SVD for image compression
33
SVD for regression
decision trees
34
CART: classification
35
CART: regression
36
random forest
information theory
37
entropy
38
cross-entropy and KL divergence
likelihood
39
probability and likelihood
40
maximum likelihood estimation
41
MLE and summary statistics
42
MLE and linear regression
43
MLE and information theory
44
MLE and regularization
45
MLE and classification
46
MLE and bayesian inference
generalization and model complexity
47
motivation
48
bias-variance tradeoff
49
overfitting and underfitting
50
cross-validation
51
data splitting
52
regularization
53
when more data changes the tradeoff
54
double descent
neural networks
55
Nielsen’s NNDL, ch.1
56
Nielsen’s NNDL, ch.2
57
Nielsen’s NNDL, ch.3A
58
Nielsen’s NNDL, ch.3B
miscellaneous
59
trend test
60
SHAP values
likelihood
46
MLE and bayesian inference
46
MLE and bayesian inference
Code
45
MLE and classification
47
motivation