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Machine learning algorithms

#machine-learning-algorithms · 56 entries. Projects, essays and notes, newest first. Page 2 of 3.

02-02-438Effective Component SizeNoteExpectation–maximisation (EM) for GMMs 02-02-439M-Step (Covariance Update)NoteExpectation–maximisation (EM) for GMMs 02-02-440M-Step (Means Update)NoteExpectation–maximisation (EM) for GMMs 02-02-441M-Step (Weights Update)NoteExpectation–maximisation (EM) for GMMs 02-02-442Feature Matrix (Phi) FormulationNoteLinear regression and maximum likelihood estimation (MLE)02-02-443Gradient of Least-Squares LossNoteLinear regression and maximum likelihood estimation (MLE)02-02-444Least-Squares Objective (Empirical Risk)NoteLinear regression and maximum likelihood estimation (MLE)02-02-445Maximum Likelihood Reconstruction (Orthogonal Projection)NoteLinear regression and maximum likelihood estimation (MLE)02-02-446Normal Equations (MLE Solution)NoteLinear regression and maximum likelihood estimation (MLE)02-02-447Ridge Regression Solution (L₂ Penalty)NoteLinear regression and maximum likelihood estimation (MLE)02-02-448General Neural Network Layer MappingNoteLogistic regression and neural networks 02-02-450AccuracyNoteModel evaluation metrics 02-02-451Coefficient of Determination (R²)NoteModel evaluation metrics 02-02-452F1-Score (Harmonic Mean)NoteModel evaluation metrics 02-02-453Mean Absolute Error (MAE)NoteModel evaluation metrics 02-02-454PrecisionNoteModel evaluation metrics 02-02-455Recall, SensitivityNoteModel evaluation metrics 02-02-456Akaike Information Criterion (AIC)NoteModel selection and information criteria 02-02-458Beta-Binomial Conjugacy PosteriorNoteModel selection and information criteria 02-02-459Posterior OddsNoteModel selection and information criteria 02-02-460Generative ModelNoteProbabilistic PCA (PPCA)02-02-461Latent Variable PriorNoteProbabilistic PCA (PPCA)02-02-462Marginal LikelihoodNoteProbabilistic PCA (PPCA)02-02-463PPCA MLE ParametersNoteProbabilistic PCA (PPCA)