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#Tag

Machine learning algorithms

#machine-learning-algorithms · 56 entries. Projects, essays and notes, newest first. Page 1 of 3.

02-02-431Eigenvector Equation (Principal Axes)NoteDimensionality reduction: principal component analysis (PCA)02-02-437E-Step (Responsibilities)NoteExpectation–maximisation (EM) for GMMs 02-02-449Logistic Sigmoid (Transfer/Activation Function)NoteLogistic regression and neural networks 02-02-457Bayesian Information Criterion (BIC)NoteModel selection and information criteria 02-02-416Marginal Likelihood (Evidence)NoteBayesian linear regression (BLR)02-02-417Parameter PosteriorNoteBayesian linear regression (BLR)02-02-418Parameter PriorNoteBayesian linear regression (BLR)02-02-419Posterior CovarianceNoteBayesian linear regression (BLR)02-02-420Posterior Mean (MAP)NoteBayesian linear regression (BLR)02-02-421Posterior Predictive DistributionNoteBayesian linear regression (BLR)02-02-422Cross-Entropy Loss (Binary)NoteClassification loss functions 02-02-423K-Means Objective, Within-Cluster Sum of Squares, WCSSNoteClustering algorithms 02-02-424Infinite Step Function (Constraint Penalty)NoteContinuous optimisation for ML constraints 02-02-425PSD Verification via Rayleigh QuotientNoteContinuous optimisation for ML constraints 02-02-426Quadratic Programming Matrix FormulationNoteContinuous optimisation for ML constraints 02-02-427Latent Variable Expected Log-LikelihoodNoteDensity estimation: Gaussian mixture models (GMM)02-02-428Log-Likelihood ObjectiveNoteDensity estimation: Gaussian mixture models (GMM)02-02-429Mixture DensityNoteDensity estimation: Gaussian mixture models (GMM)02-02-430Data CenteringNoteDimensionality reduction: principal component analysis (PCA)02-02-432Low-Rank Approximation (SVD)NoteDimensionality reduction: principal component analysis (PCA)02-02-433Projection (Encoding)NoteDimensionality reduction: principal component analysis (PCA)02-02-434Reconstruction (Decoding)NoteDimensionality reduction: principal component analysis (PCA)02-02-435Residual Error (Truncated Eigenvalues)NoteDimensionality reduction: principal component analysis (PCA)02-02-436Sample Covariance MatrixNoteDimensionality reduction: principal component analysis (PCA)