likelihood
45
MLE and classification
Statistics and Machine Learning
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data
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one-sample t-test
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independent samples t-test
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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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confidence interval
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basic concepts
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the geometry of regression
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linear mixed effect model
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Bayes’ theorem from the ground up
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monty hall
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CART: classification
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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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motivation
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bias-variance tradeoff
49
overfitting and underfitting
50
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
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
45
MLE and classification
45
MLE and classification
Code
44
MLE and regularization
46
MLE and bayesian inference