Pattern Recognition and Machine Learning

Chapter 1 — Introduction

1. 20 May 22 Introduction - Polynomial Curve Fitting Bishop · Chapter 1 — Introduction Bishop
2. 23 May 22 Introduction - Probability Theory Bishop · Chapter 1 — Introduction Bishop
3. 29 May 22 Introduction - Model Selection & Curse of Dimensionality Bishop · Chapter 1 — Introduction Bishop
4. 04 Jun 22 Introduction - Decision Theory Bishop · Chapter 1 — Introduction Bishop
5. 10 Jun 22 Introduction - Information Theory Bishop · Chapter 1 — Introduction Bishop

Chapter 2 — Probability Distributions

1. 11 Jun 22 Probability Distributions - Binary Variables Bishop · Chapter 2 — Probability Distributions Bishop
2. 12 Jun 22 Probability Distributions - Multinomial Variables Bishop · Chapter 2 — Probability Distributions Bishop
3. 13 Jun 22 Probability Distributions - The Gaussian Distribution: Part 1 Bishop · Chapter 2 — Probability Distributions Bishop
4. 14 Jun 22 Probability Distributions - The Gaussian Distribution: Part 2 Bishop · Chapter 2 — Probability Distributions Bishop
5. 15 Jun 22 Probability Distributions - The Gaussian Distribution: Part 3 Bishop · Chapter 2 — Probability Distributions Bishop
6. 16 Jun 22 Probability Distributions - The Gaussian Distribution: Part 4 Bishop · Chapter 2 — Probability Distributions Bishop
7. 17 Jun 22 Probability Distributions - The Gaussian Distribution: Part 5 Bishop · Chapter 2 — Probability Distributions Bishop
8. 18 Jun 22 Probability Distributions - The Exponential Family Bishop · Chapter 2 — Probability Distributions Bishop
9. 19 Jun 22 Probability Distributions - Nonparametric Methods Bishop · Chapter 2 — Probability Distributions Bishop

Chapter 3 — Linear Models for Regression

1. 20 Jun 22 Linear Models for Regression - Linear Basis Function Models : Part 1 Bishop · Chapter 3 — Linear Models for Regression Bishop
2. 21 Jun 22 Linear Models for Regression - Linear Basis Function Models : Part 2 Bishop · Chapter 3 — Linear Models for Regression Bishop
3. 23 Jun 22 Linear Models for Regression - Bias-Variance Decomposition Bishop · Chapter 3 — Linear Models for Regression Bishop
4. 25 Jun 22 Linear Models for Regression - Bayesian Linear Regression Bishop · Chapter 3 — Linear Models for Regression Bishop
5. 26 Jun 22 Linear Models for Regression - Bayesian Model Comparison Bishop · Chapter 3 — Linear Models for Regression Bishop
6. 27 Jun 22 Linear Models for Regression - Evidence Approximation & Limitations of Fixed Basis Function Bishop · Chapter 3 — Linear Models for Regression Bishop

Chapter 4 — Linear Models for Classification

1. 28 Jun 22 Linear Models for Classification - Discriminant Functions Bishop · Chapter 4 — Linear Models for Classification Bishop
2. 29 Jun 22 Linear Models for Classification - Discriminant Functions (Part 2) Bishop · Chapter 4 — Linear Models for Classification Bishop
3. 30 Jun 22 Linear Models for Classification - Least Squares for Classification Bishop · Chapter 4 — Linear Models for Classification Bishop
4. 01 Jul 22 Linear Models for Classification - Fisher’s Linear Discriminant Bishop · Chapter 4 — Linear Models for Classification Bishop
5. 02 Jul 22 Linear Models for Classification - The Perceptron Algorithm Bishop · Chapter 4 — Linear Models for Classification Bishop
6. 03 Jul 22 Linear Models for Classification - Probabilistic Generative Models Bishop · Chapter 4 — Linear Models for Classification Bishop
7. 04 Jul 22 Linear Models for Classification - Probabilistic Generative Models (Maximum Likelihood Solution) Bishop · Chapter 4 — Linear Models for Classification Bishop
8. 05 Jul 22 Linear Models for Classification - Probabilistic Discriminative Models Bishop · Chapter 4 — Linear Models for Classification Bishop
9. 06 Jul 22 Linear Models for Classification - The Laplace Approximation & Bayesian Logistic Regression Bishop · Chapter 4 — Linear Models for Classification Bishop

Chapter 5 — Neural Networks

1. 09 Jul 22 Neural Networks - Feed-forward Network Functions Bishop · Chapter 5 — Neural Networks Bishop
2. 11 Jul 22 Neural Networks - Network Training Bishop · Chapter 5 — Neural Networks Bishop
3. 13 Jul 22 Neural Networks - Error Backpropagation Bishop · Chapter 5 — Neural Networks Bishop
4. 15 Jul 22 Neural Networks - The Hessian Matrix Bishop · Chapter 5 — Neural Networks Bishop
5. 17 Jul 22 Neural Networks - Regularization in Neural Networks Bishop · Chapter 5 — Neural Networks Bishop
6. 19 Jul 22 Neural Networks - Mixture Density Networks & Bayesian Neural Networks Bishop · Chapter 5 — Neural Networks Bishop

Chapter 6 — Kernel Methods

1. 25 Jul 22 Kernel Methods - Dual Representations Bishop · Chapter 6 — Kernel Methods Bishop
2. 29 Jul 22 Kernel Methods - Constructing Kernels & Radial Basis Function Networks Bishop · Chapter 6 — Kernel Methods Bishop
3. 04 Aug 22 Kernel Methods - Gaussian Process Bishop · Chapter 6 — Kernel Methods Bishop

Chapter 7 — Sparse Kernel Methods

1. 14 Aug 22 Sparse Kernel Methods - Lagrange Multipliers Bishop · Chapter 7 — Sparse Kernel Methods Bishop
2. 18 Aug 22 Sparse Kernel Methods - Maximum Margin Classifiers Bishop · Chapter 7 — Sparse Kernel Methods Bishop
3. 23 Aug 22 Sparse Kernel Methods - Maximum Margin Classifiers: Overlapping Class Distributions Bishop · Chapter 7 — Sparse Kernel Methods Bishop
4. 28 Aug 22 Sparse Kernel Methods - Maximum Margin Classifiers: Relation to Logistic Regression, Multiclass SVMs, SVMs for Regression Bishop · Chapter 7 — Sparse Kernel Methods Bishop

Chapter 8 — Graphical Models

1. 05 Sep 22 Graphical Models - Bayesian Networks Bishop · Chapter 8 — Graphical Models Bishop
2. 11 Sep 22 Graphical Models - Conditional Independence Bishop · Chapter 8 — Graphical Models Bishop
3. 15 Sep 22 Graphical Models - Markov Random Fields Bishop · Chapter 8 — Graphical Models Bishop
4. 20 Sep 22 Graphical Models - Inference in Graphical Models Bishop · Chapter 8 — Graphical Models Bishop
5. 04 Oct 22 Graphical Models - The Sum-product Algorithm, The Max-Sum Algorithm Bishop · Chapter 8 — Graphical Models Bishop

Chapter 9 — Mixture Models and Expectation Maximization

1. 11 Oct 22 Mixture Models and Expectation Maximization - K-means Clustering Bishop · Chapter 9 — Mixture Models and Expectation Maximization Bishop
2. 18 Oct 22 Mixture Models and Expectation Maximization - Mixtures of Gaussians Bishop · Chapter 9 — Mixture Models and Expectation Maximization Bishop
3. 04 Nov 22 Mixture Models and Expectation Maximization - An Alternative View of EM Bishop · Chapter 9 — Mixture Models and Expectation Maximization Bishop
4. 18 Nov 22 Mixture Models and Expectation Maximization - The EM Algorithm in General Bishop · Chapter 9 — Mixture Models and Expectation Maximization Bishop