BOOK: "Machine Learning: An Algorithmic Perspective".

From : http://www-ist.massey.ac.nz/smarsland/MLbook.html

I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence.
There are lots of Python code examples in the book, and the code is available here. Where special datasets are used they are provided with the code, and there are links to additional datasets at the bottom of the page.

Option 1: Eclipse zip file of all code
Option 2: Choose what you want from here:

• Chapter 2 (Linear Discriminants):
  • The Perceptron
  • The Linear Regressor
  • Another Perceptron (for use with logic.py)
  • Demonstration of Perceptron with logic functions
  • Demonstration of Linear Regressor with logic functions
  • Demonstration of Perceptron with Pima Indian dataset
  • Demonstration of Linear Regressor with auto-mpg dataset
  • The numbers dataset Noisy versions of the numbers More noisy numbers Visualisation of the numbers dataset
  • Training data for the prostate dataset Test data for the prostate dataset (variables are log cancer volume, log prostate weight, age, lbph, svi, lcp, Gleason score, pgg45 and the last one is response lpsa)
• Chapter 3 (The Multi-Layer Perceptron):
  • The Multi-Layer Perceptron
  • Demonstration of the MLP on logic functions
  • Demonstration of the MLP for classification on the Iris dataset
  • Demonstration of the MLP for regression on data from a sine wave
  • Demonstration of the MLP for time series on the Palmerston North Ozone dataset
  • The Palmerston North ozone dataset
• Chapter 4 (Radial Basis Functions):
  • The Radial Basis Function
  • Linear Least Squares Fitting
  • Demonstration of the RBF on the Iris dataset
• Chapter 6 (Learning with Trees):
  • The decision tree
  • Demonstration of the decision tree on the Party dataset
  • The Party dataset
• Chapter 7 (Decision by Committee: Ensemble Learning):
  • The Boosting algorithm
  • The Bagging algorithm
  • A decision tree with weights
  • Demonstration of bagging and stumping on the Party dataset
  • Demonstration of bagging on the Car Safety dataset
• Chapter 8 (Probability and Learning):
  • The Gaussian Mixture Model
  • The k-Nearest Neighbour Algorithm
  • The k-Nearest Neighbour Smoother
  • The kd-Tree Algorithm
  • Plot of a 1D Gaussian distribution
  • Plots of 2D Gaussian distributions
  • The Ruapehu dataset (thanks to Mark Bebbington)
• Chapter 9 (Unsupervised Learning):
  • The k-Means Algorithm
  • The k-Means Neural Network
  • The Self-Organising Map
  • A Simple 2D Demonstration of the SOM
  • Demonstration of k-Means and the SOM on the Iris dataset
  • More demonstrations of the SOM
• Chapter 10 (Dimensionality Reduction):
  • Linear Discriminant Analysis
  • Principal Components Analysis
  • Factor Analysis
  • Kernel Principal Components Analysis
  • Locally Linear Embedding
  • Isomap
  • Demonstration of PCA
  • Demonstration of kernel PCA on a set of circular data
  • Demonstration of the algorithms on the Iris dataset
• Chapter 11 (Optimisation and Search):
  • Steepest Descent
  • Newton's method
  • Levenberg-Marquarft
  • Conjugate Gradients
  • Demonstration of Levenberg-Marquardt on a least-squares fitting problem
  • Demonstration of four solution methods for the Travelling Salesman Problem
• Chapter 12 (Evolutionary Learning):
  • The Genetic Algorithm
  • A Runner for the GA
  • Population-Based Incremental Learning
  • A knapsack problem fitness function
  • The four peaks fitness function
  • Exhaustive search algorithm to solve the knapsack problem
  • A greedy algorithm to solve the knapsack problem
• Chapter 13 (Reinforcement Learning):
  • The SARSA algorithm
  • The TD(0) algorithm
  • Demonstration of the SARSA algorithm on the Cliff problem
  • Demonstration of the TD(0) algorithm on the Cliff problem
• Chapter 14 (Markov Chain Monte Carlo Methods):
  • The Linear Congruential Pseudo-Random Number Generator
  • The Box-Muller method of constructing Gaussian-distributed pseudo-random numbers
  • The Rejection Sampling Algorithm
  • The Importance Sampling Algorithm
  • The Sampling-Importance-Resampling Algorithm
  • The Metropolis-Hastings Algorithm
  • The Gibbs Sampler
• Chapter 15 (Graphical Models):
  • The Gibbs Sampler for the Exam Panic dataset
  • The Hidden Markov Model
  • The Kalman Filter
  • The Markov Random Field for Image Denoising
  • A demonstration of finding paths in graphs
  • An image for denoising

Many of the datasets used in the book are available from the UCI Machine Learning Repository.