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Resources
Netlab
Book
.
Netlab: Algorithms for Pattern Recognition
Chapter 1: Introduction
Introduction to Matlab: Matlab Basics; Matlab Plotting; Programming in Matlab; Programming Facilities.
The Netlab Toolbox: Overview of Netlab; Generic Functions.
Worked Example: Data Normalisation
Chapter 2: Parameter Optimisation Algorithms
Controlling the Algorithms: Information Display; Termination Criteria; Extra Arguments and Return Values; Demonstration Program. Quadratic Approximation at a Minimum Line Search: Precision; Line Minimisation Algorithms; Bracketing the Minimum. Batch Gradient Descent Conjugate Gradients Scaled Conjugate Gradients QuasiNewton Methods Optimisation and Neural Networks: General Purpose Optimisation Algorithms; Online Gradient Descent. Worked Example: Constrained Optimisation
Chapter 3: Density Modelling and Clustering
Gaussian Mixture Models: Theory; Implementation.
Computing Probabilities: GMM Activations; GMM Probabilities; GMM Posteriors.
EM Training Algorithm: Algorithm Theory; Initialisation; Netlab EM Implementation.
Demonstrations of GMM: EM Algorithm; Density Modelling.
Kmeans Clustering: Algorithm and Netlab Implementation; Demonstration Program.
Knearestneighbour: Algorithm and Netlab Implementation; Demonstration Program.
Worked Examples: Classification with Density Models; Novelty Detection.
Chapter 4: Single Layer Networks
The Single Layer Feedforward Network: Netlab Implementation; Forward Propagation. Error Functions Error Gradient Calculation Evaluating Other Derivatives: Network Activation Derivatives; The Hessian Matrix. Iterated Reweighted Least Squares Training Demonstration Programs: Twoclass Problem; Threeclass Problem. Worked Example: Training Regularised Models
Chapter 5: The Multilayer Perceptron
The Twolayer Feedforward Network: Definition; Network Creation and Initialisation; Manipulating Weights; Forward Propagation. Error Functions and Network Training Error Gradient Calculation Evaluating other Derivatives The Hessian Matrix: Fast Multiplication by the Hessian; Netlab Implementation. Demonstration Programs: Regression Demonstration; Classification Demonstration. Mixture Density Networks: Model Structure; Network Creation and Initialisation; Forward Propagation; Conditional Probabilities; Error and Gradient Calculation; Demonstration Program. Worked Example: Adding Direct Connections
Chapter 6: Radial Basis Functions
The RBF Network: Theory; Netlab}\ Implementation. Special Purpose Training Algorithms: Basis Function Optimisation; Output Weight Optimisation; Netlab Implementation. Error and Error Gradient Calculation Evaluating Other Derivatives: Output Derivatives; Network Jacobian; Network Hessian. Demonstration Program Worked Examples: Linear Smoothing; Dual Basis Functions; Characterising Network Complexity.
Chapter 7: Visualisation and Latent Variable Models
Principal Component Analysis: Theory; Netlab Implementation; Examples. Probabilistic Principal Component Analysis: Probabilistic PCA; PPCA Implementation; Mixture of PPCA; Mixture of PPCA Implementation. Generative Topographic Mapping: The GTM Model; Model Creation and Initialisation; Computing Probabilities; EM Algorithm; Magnification Factors; Demonstration Programs. Topographic Projection: Neuroscale Algorithm; Neuroscale Implementation; Demonstration of Neuroscale. Worked Example: Canonical Variates.
Chapter 8: Sampling
Monte Carlo Integration Basic Sampling: Random Number Generators; Transformation Methods; Rejection Sampling; Importance Sampling. Markov Chain Sampling: Markov Chain Fundamentals; Gibbs Sampling; MetropolisHastings Algorithm; Hybrid Monte Carlo. Demonstration Programs: MetropolisHastings Sampling; Hybrid Monte Carlo Sampling. Worked Example: Convergence Diagnostics
Chapter 9: Bayesian Techniques
Principles of Bayesian Inference Priors for Neural Networks: Theory of Priors; Netlab Implementation of Priors; Demonstration of Gaussian Priors; Masks and Weight Manipulation. Computing Error and Gradient Functions The Evidence Procedure: Theory; Netlab Implementation. Predictions and Error Bars: Predictions for Regression; Predictions for Classification; Netlab Implementation. Demonstrations of Evidence Procedure: The Evidence Procedure for Regression; The Evidence Procedure for Classification; Automatic Relevance Determination. Monte Carlo Methods Demonstration of Hybrid Monte Carlo for MLPs Worked Example: Improved Classification Approximation
Chapter 10: Gaussian Processes
Bayesian Regression: Weight Space View; Function Space View. Theory of Gaussian Processes: Model Definition; Learning Hyperparameters. Netlab Implementation: Model Creation and Initialisation; Making Predictions; Model Training. Demonstration Programs: Regression Demonstration; ARD Demonstration. Worked Example: GPs for Classification
Appendices: Linear Algebra and Matrices; Algorithm Error Analysis.
Engineering & Applied Science
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Nonlinearity and Complexity Research Group
Resources
Netlab
Book
Contents
Worked Examples