Amit Rajan

Machine Learning | Blockchain

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

Logistic Regression

Logistic Regression: Derivation

Performance Metrics for Classification Algorithms

Performance Metrics for Classification Algorithms

Naive Bayes Classifier

Naive Bayes Classifier

ISLR Chapter 4: Classification (Part 4: Exercises- Applied)

ISLR Classification

ISLR Chapter 4: Classification (Part 3: Exercises- Conceptual)

ISLR Classification

ISLR Chapter 4: Classification (Part 2: Linear Discriminant Analysis)

ISLR Classification

ISLR Chapter 4: Classification (Part 1: Logistic Regression)

ISLR Classification