An Introduction to Pattern Recognition and Machine Learning - List of Examples

The book contains a large number of examples, interspersed throughout the text, with a case study and worked numerical lab at the end of every chapter:

Case Study 2: Biometrics
Case Study 3: The Netflix Prize
Case Study 4: Defect Detection
Case Study 5: Image Searching
Case Study 6: Hand-Writing Recognition
Case Study 7: Object Recognition
Case Study 8: Medical Assessments
Case Study 9: Autonomous Vehicles
Case Study 10: Digital Communications
Case Study 11: Interpretability and Ethics of Large Networks
Case Study 12: Ancient Text Analysis: Who Wrote What?

Example 2.1: Pattern Recognition of Text
Example 2.2: Pattern Recognition of the Mind
Example 2.3: What is a Class
Example 2.4: The Types of Pattern Recognition

Example 3.1: Robustness in Learning
Example 3.2: Regression and Classification
Example 3.3: An overview of the use of data in learning
Example 3.4: Wrapper-Based Learning

Example 4.1: Face Recognition
Example 4.2: How to Form an Ellipsoid
Example 4.3: Quadratic Forms
Example 4.4: Ellipsoids and Real Data
Example 4.5: Class Data, Covariances, Eigendecompositions, and Ellipses

Example 6.1: Euclidean Distance and Measurement Units

Example 7.1: Learning of Mixture Models
Example 7.2: Nonlinear Maximum Likelihood

Example 8.1: Are we Classifying or Estimating
Example 8.2: Classifier Probability of Error
Example 8.3: Classifier Probability of Error for Gaussian Statistics

Example 9.1: Data Augmentation
Example 9.2: Receiver Operating Characteristics

Example 10.1: Mean-Squared Error Discriminant
Example 10.2: Radial Basis Functions

Example 11.1: Bootstrap and Estimation
Example 11.2: Biological and Artificial Neurons
Example 11.3: Illusions in Machine Learninge

Example 12.1: Vector Quantization
Example 12.2: Bag-of-Words and Visual Representation

Example B.1: Power Laws and Infinite Variance

Lab 2: The Iris Dataset
Lab 3: Overfitting and Underfitting
Lab 4: Working with Random Numbers
Lab 5: Extracting Features and Plotting Classes
Lab 6: Distance-Based Classifiers
Lab 7: Parametric and Nonparametric Estimation
Lab 8: Statistical and Distance-Based Classifiers
Lab 9: Leave-One-Out Validation
Lab 10: Discriminants
Lab 11: Ensemble Classifiers
Lab 12: Clustering