likelihood
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MLE and classification
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
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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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equivalence
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partitioning of the sum of squares
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linear mixed effect model
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logistic regression
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logistic 2d
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correlation
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correlation and linear regression
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cosine
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significance (p-value)
bayes
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Bayes’ theorem from the ground up
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parametric generative classification
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odds and log likelihood
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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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CART: regression
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random forest
information theory
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entropy
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cross-entropy and KL divergence
likelihood
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probability and likelihood
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maximum likelihood estimation
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MLE and summary statistics
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MLE and linear regression
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MLE and information theory
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MLE and regularization
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MLE and classification
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MLE and bayesian inference
generalization and model complexity
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bias-variance tradeoff
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overfitting and underfitting
48
cross-validation
49
data splitting
50
regularization
51
when more data changes the tradeoff
52
double descent
miscellaneous
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trend test
likelihood
44
MLE and classification
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MLE and classification
Code
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MLE and regularization
45
MLE and bayesian inference