Amit Rajan

Machine Learning | Blockchain

Mixture Models and Expectation Maximization - The EM Algorithm in General

Pattern Recognition (Bishop): Chapter 9

Mixture Models and Expectation Maximization - An Alternative View of EM

Pattern Recognition (Bishop): Chapter 9

Mixture Models and Expectation Maximization - Mixtures of Gaussians

Pattern Recognition (Bishop): Chapter 9

Mixture Models and Expectation Maximization - K-means Clustering

Pattern Recognition (Bishop): Chapter 9

Graphical Models - The Sum-product Algorithm, The Max-Sum Algorithm

Pattern Recognition (Bishop): Chapter 8

Graphical Models - Inference in Graphical Models

Pattern Recognition (Bishop): Chapter 8

Graphical Models - Markov Random Fields

Pattern Recognition (Bishop): Chapter 8

Graphical Models - Conditional Independence

Pattern Recognition (Bishop): Chapter 8

Graphical Models - Bayesian Networks

Pattern Recognition (Bishop): Chapter 8

Sparse Kernel Methods - Maximum Margin Classifiers: Relation to Logistic Regression, Multiclass SVMs, SVMs for Regression

Pattern Recognition (Bishop): Chapter 7

Sparse Kernel Methods - Maximum Margin Classifiers: Overlapping Class Distributions

Pattern Recognition (Bishop): Chapter 7

Sparse Kernel Methods - Maximum Margin Classifiers

Pattern Recognition (Bishop): Chapter 7

Sparse Kernel Methods - Lagrange Multipliers

Pattern Recognition (Bishop): Chapter 7

Kernel Methods - Gaussian Process

Pattern Recognition (Bishop): Chapter 6

Kernel Methods - Constructing Kernels & Radial Basis Function Networks

Pattern Recognition (Bishop): Chapter 6

Kernel Methods - Dual Representations

Pattern Recognition (Bishop): Chapter 6

Neural Networks - Mixture Density Networks & Bayesian Neural Networks

Pattern Recognition (Bishop): Chapter 5

Neural Networks - Regularization in Neural Networks

Pattern Recognition (Bishop): Chapter 5

Neural Networks - The Hessian Matrix

Pattern Recognition (Bishop): Chapter 5

Neural Networks - Error Backpropagation

Pattern Recognition (Bishop): Chapter 5

Neural Networks - Network Training

Pattern Recognition (Bishop): Chapter 5

Neural Networks - Feed-forward Network Functions

Pattern Recognition (Bishop): Chapter 5

Linear Models for Clasification - The Laplace Approximation & Bayesian Logistic Regression

Pattern Recognition (Bishop): Chapter 4

Linear Models for Clasification - Probabilistic Discriminative Models

Pattern Recognition (Bishop): Chapter 4

Linear Models for Clasification - Probabilistic Generative Models (Maximum Likelihood Solution)

Pattern Recognition (Bishop): Chapter 4

Linear Models for Clasification - Probabilistic Generative Models

Pattern Recognition (Bishop): Chapter 4

Linear Models for Clasification - The Perceptron Algorithm

Pattern Recognition (Bishop): Chapter 4

Linear Models for Clasification - Fisher’s Linear Discriminant

Pattern Recognition (Bishop): Chapter 4

Linear Models for Clasification - Least Squares for Classification

Pattern Recognition (Bishop): Chapter 4

Linear Models for Clasification - Discriminant Functions

Pattern Recognition (Bishop): Chapter 4

Linear Models for Clasification - Discriminant Functions

Pattern Recognition (Bishop): Chapter 4

Linear Models for Regression - Evidence Approximation & Limitations of Fixed Basis Function

Pattern Recognition (Bishop): Chapter 3

Linear Models for Regression - Bayesian Model Comparison

Pattern Recognition (Bishop): Chapter 3

Linear Models for Regression - Bayesian Linear Regression

Pattern Recognition (Bishop): Chapter 3

Linear Models for Regression - Bias-Variance Decomposition

Pattern Recognition (Bishop): Chapter 3

Linear Models for Regression - Linear Basis Function Models : Part 2

Pattern Recognition (Bishop): Chapter 3

Linear Models for Regression - Linear Basis Function Models : Part 1

Pattern Recognition (Bishop): Chapter 3

Probability Distributions - Nonparametric Methods

Pattern Recognition (Bishop): Chapter 2

Probability Distributions - The Exponential Family

Pattern Recognition (Bishop): Chapter 2

Probability Distributions - The Gaussian Distribution: Part 5

Pattern Recognition (Bishop): Chapter 2

Probability Distributions - The Gaussian Distribution: Part 4

Pattern Recognition (Bishop): Chapter 2

Probability Distributions - The Gaussian Distribution: Part 3

Pattern Recognition (Bishop): Chapter 2

Probability Distributions - The Gaussian Distribution: Part 2

Pattern Recognition (Bishop): Chapter 2

Probability Distributions - The Gaussian Distribution: Part 1

Pattern Recognition (Bishop): Chapter 2

Probability Distributions - Multinomial Variables

Pattern Recognition (Bishop): Chapter 2

Probability Distributions - Binary Variables

Pattern Recognition (Bishop): Chapter 2

Introduction - Information Theory

Pattern Recognition (Bishop): Chapter 1

Introduction - Decision Theory

Pattern Recognition (Bishop): Chapter 1

Introduction - Model Selection & Curse of Dimensionality

Pattern Recognition (Bishop): Chapter 1

Introduction - Probability Theory

Pattern Recognition (Bishop): Chapter 1

Introduction - Polynomial Curve Fitting

Pattern Recognition (Bishop): Chapter 1