generalization and model complexity
58
double descent
Statistics and Machine Learning
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data
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height data
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weight data
foundations
3
the language of random variables
4
expectation
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variance and moments
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population and samples
hypothesis testing
7
one-sample t-test
8
independent samples t-test
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statistical power
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the problem with t-test
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permutation test
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numpy vs pandas
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exact vs. Monte Carlo permutation tests
confidence interval
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basic concepts
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analytical confidence interval
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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
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correlation and linear regression
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bayes
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Bayes’ theorem from the ground up
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logistic connection
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conjugate prior
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the boy-girl paradox
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monty hall
svd and pca
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SVD for image compression
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SVD for regression
decision trees
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CART: classification
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information theory
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entropy
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cross-entropy and KL divergence
likelihood
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maximum likelihood estimation
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MLE and classification
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MLE and bayesian inference
generalization and model complexity
51
motivation
52
bias-variance tradeoff
53
overfitting and underfitting
54
cross-validation
55
data splitting
56
regularization
57
when more data changes the tradeoff
58
double descent
neural networks
59
Nielsen’s NNDL, ch.1
60
Nielsen’s NNDL, ch.2
61
Nielsen’s NNDL, ch.3A
62
Nielsen’s NNDL, ch.3B
miscellaneous
63
trend test
64
SHAP values
generalization and model complexity
58
double descent
58
double descent
Code
57
when more data changes the tradeoff
59
Nielsen’s NNDL, ch.1