likelihood
48
MLE and regularization
Statistics and Machine Learning
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data
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the language of random variables
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expectation
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variance and moments
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population and samples
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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numpy vs pandas
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confidence interval
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the geometry of regression
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R-squared
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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
48
MLE and regularization
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MLE and classification
50
MLE and bayesian inference
generalization and model complexity
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motivation
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bias-variance tradeoff
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overfitting and underfitting
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cross-validation
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data splitting
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regularization
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when more data changes the tradeoff
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double descent
neural networks
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Nielsen’s NNDL, ch.1
60
Nielsen’s NNDL, ch.2
61
Nielsen’s NNDL, ch.3A
62
Nielsen’s NNDL, ch.3B
miscellaneous
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trend test
64
SHAP values
likelihood
48
MLE and regularization
48
MLE and regularization
Code
47
MLE and information theory
49
MLE and classification