PatterRecognition-C14-Combining-Models

Keywords: Boosting, Conditional Mixture Models(linear regression, logistic regression), EM Algorithm

PatterRecognition-C13-Sequential-Data

Keywords: Hidden Markov Models, Python

PatterRecognition-C12-Continuous-Latent-Variables

Keywords: Conventional PCA, Probabilistic PCA, Nonlinear Latent Variable Models, Python

PatterRecognition-C11-Sampling-Methods

Keywords: Markov Chain Monte Carlo, Python

PatterRecognition-C10-Approximate-Inference

Keywords: Variational Linear Regression, Variational Logistic Regression, Variational Inference, Python

PatterRecognition-C9-Mixture-Models-and-EM

Keywords: K-means Clustering, Mixtures of Gaussians, EM Algorithm, Python

PatterRecognition-C8-Graphical-Models

Keywords: Bayesian Networks, Markov Random Fields, Inference, Python

PatterRecognition-C7-Sparse-Kernel-Machines

Keywords: SVM, RVM, Sparse Kernel technique, Python

PatterRecognition-C6-Kernel-Methods

Keywords: Gaussian processes, Radial Basis Function Networks, Laplace approximation, Python

PatterRecognition-C5-Neural-Networks

Keywords: Gradient descent optimization, Error backpropagation, Hessian Matrix, Jacobian Matrix, Regularization, Mixture Density Network, Bayesian Neural Network, Python

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