Amit Rajan

Machine Learning | Blockchain

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

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