Machine learning
Machine learning is an area of study that deals with the making predictions using algorithms. It analyses data to automates analytical model building. It aims to guesses to be useful.
Pattern recognition
It is a process of recognition of patterns using a Machine Learning algorithm. It may be defined as is the ability to trace arrangements of characteristics or data that produce information for a given system or data set.it is a ramification of machine learning that enforce the recognition of data in a particular situation
Course outline
Introduction
Example: Polynomial Curve Fitting
Probability Theory
Probability densities
Expectations and covariances
Bayesian probabilities
The Gaussian distribution
Curve fitting re-visited
Bayesian curve fitting
Model Selection
The Curse of Dimensionality
Decision Theory
Minimizing the misclassification rate
Minimizing the expected loss
The reject option and decision
Loss functions for regression
Information Theory
Relative entropy and mutual information
Probability Distributions
Binary Variables
The beta distribution
Multinomial Variables
The Dirichlet distribution
The Gaussian Distribution
Conditional Gaussian distributions
Marginal Gaussian distributions
Bayes’ theorem for Gaussian variables
Maximum likelihood for the Gaussian
Sequential estimation
Bayesian inference for the Gaussian
Student’s t-distribution
Mixtures of Gaussians
The Exponential Family
Maximum likelihood and sufficient statistics
Conjugate priors
Noninformative priors
Linear Models for Regression
Linear Basis Function Models
Maximum likelihood and least squares
Geometry of least squares
Sequential learning
Regularized least squares
Multiple outputs
The Bias-Variance Decomposition
Bayesian Linear Regression
Predictive distribution
Equivalent kernel
Bayesian Model Comparison
Evaluation of the evidence function
Maximizing the evidence function
Effective number of parameters
Limitations of Fixed Basis Functions
Linear Models for Classification
Discriminant Functions
Two classes
Multiple classes
Least squares for classification
Fisher’s linear discriminant
Relation to least squares
Fisher’s discriminant for multiple classes
The Percheron algorithm
Probabilistic Generative Models
Continuous inputs
Maximum likelihood solution
Discrete features
Exponential family
Probabilistic Discriminative Models
Fixed basis functions regression
Iterative re weighted least squares
Multi class logistic regression
Pr obit regression
Canonical link function
The Laplace Approximation
Model comparison and BIC
Bayesian Logistic Regression
Laplace approximation
Predictive distribution
Neural Networks
Feed-forward Network Functions
Network Training
Error Back propagation
The Hessian Matrix
Regularization in Neural Networks
Mixture Density Networks
Bayesian Neural Networks
Kernel Methods
Dual Representations
Constructing Kernels
Radial Basis Function Networks
Nadaraya-Watson model
Gaussian Processes
Linear regression revisited
Gaussian processes for regression
Learning the hyper parameters
Automatic relevance determination
Gaussian processes for classification
Laplace approximation
Connection to neural networks
Sparse Kernel Machines
Maximum Margin Classifiers
Overlapping class distributions
Relation to logistic regression
Multi class SVMs
SVMs for regression
Computational learning theory
Relevance Vector Machines
RVM for regression
Analysis of sparsity
Graphical Models
Bayesian Networks
Conditional Independence
Markov Random Fields
Inference in Graphical Models
Mixture Models and EM
K-means Clustering
Image segmentation and compression
Mixtures of Gaussians
Maximum likelihood
EM for Gaussian mixtures
An Alternative View of EM
Gaussian mixtures revisited
Relation to K-means
Mixtures of Bernoulli distributions
EM for Bayesian linear regression
The EM Algorithm in General
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