# Statistical Learning Theory - Introduction to Pattern Recognition - Lecture Slides

The main points are:Statistical Learning Theory, Pattern Classification Algorithms, Risk Minimization Framework, Linear Models, Perceptron Algorithm, Augumented Feature Vectors, Simplicity of Notation, Squared-Error Loss Function

# Neural Network Models - Introduction to Pattern Recognition - Lecture Slides

The main points are:Nonlinear Functions, Continuous Function, Radial Basis Functio, 3-Layer Feedforward Network, Output of Network, Parameter Vector, Techniques for Interpolation, Linear Equations, Perfect Interpolation Property

# Nonlinear Functions - Introduction to Pattern Recognition - Lecture Slides

The main points are:Nonlinear Functions, Continuous Function, Radial Basis Functio, 3-Layer Feedforward Network, Output of Network, Parameter Vector, Techniques for Interpolation, Linear Equations, Perfect Interpolation Property

# Statistical Learning Theory - Introduction to Pattern Recognition - Lecture Slides

The main points are:Statistical Learning Theory, Pattern Classification Algorithms, Risk Minimization Framework, Linear Models, Perceptron Algorithm, Augumented Feature Vectors, Simplicity of Notation, Squared-Error Loss Function

# Neural Network Models - Introduction to Pattern Recognition - Lecture Slides

The main points are:Nonlinear Functions, Continuous Function, Radial Basis Functio, 3-Layer Feedforward Network, Output of Network, Parameter Vector, Techniques for Interpolation, Linear Equations, Perfect Interpolation Property

# Nonlinear Functions - Introduction to Pattern Recognition - Lecture Slides

The main points are:Nonlinear Functions, Continuous Function, Radial Basis Functio, 3-Layer Feedforward Network, Output of Network, Parameter Vector, Techniques for Interpolation, Linear Equations, Perfect Interpolation Property

# Maximum Likelihood Estimation - Introduction to Pattern Recognition - Lecture Slides

The main points are:Maximum Likelihood Estimation, Bayes Classifier, Bayesian Estimation, Density of Parameter, Parameter Estimation, Conjugate Prior, Class Conditional Densities, Maximum Aposteriori Probability, Gaussian Density

# Non-Parametric Estimation - Introduction to Pattern Recognition - Lecture Slides

The main points are:Non-Parametric Estimation, Density Functions, Kernel-Density Estimate, Parzen Window, Unit Hypercube, Data Points Falling, Kind of Generalization, Erecting Bins, D-Dimensional Gaussian Density, Gaussian Kernel

# Introduction - Programming Using C Sharp - Lecture Slides

The key points in the Advanced Algorithms are:Introduction, Programming, Oriented Programs, Algorithms, Arabic-Originated Word, Step-By-Step Process, Cooking Rice, Complexity and Efficiency, Number, Needed

# Generics Collections - Programming Using C Sharp - Lecture Slides

The key points in the Advanced Algorithms are:Generics Collections, Different Types, Generic Method, Array, Sorting, Type Checking, Generics, Compile-Time Type Safety, Generic Name, Genericmethods

# Files and Streams - Programming Using C Sharp - Lecture Slides

The key points in the Advanced Algorithms are:Files and Streams, Binary Digit, Persistent Data, Character Set, Record, Related Records, Record Key, Sequential File, Data, Sequential Stream

# Exceptions - Programming Using C Sharp - Lecture Slides

The key points in the Advanced Algorithms are:Exceptions, Error Handling, Old Way, Exception Handling, Error-Handling Code, Syntax, Block, Code, Exception-Recovery Operations, Single

# Control Statements - Programming Using C Sharp - Lecture Slides

The key points in the Advanced Algorithms are:Control Statements, Switch, While, Do-While, Execute a Statement, Depending, Bank Account, Condition Holds, Many Statements, Sell Dollar