CONTENIDO:
1. Introduction
If Data Had Mass, The Earth Would Be a Black Hole
Learning
Types of Machine Learning
Supervised Learning
The Brain and the Neuron
2. Linear Discriminants
Preliminaries
The Perceptron
Linear Separability
Linear Regression
3. The Multi-Layer Perceptron
Going Forwards
Going Backwards: Back-propagation of Error
The Multi-Layer Perceptron in Practice
Examples of Using the MLP
Overview
Back-propagation Properly
4. Radial Basis Functions and Splines
Concepts
The Radial Basis Function (RBF) Network
The Curse of Dimensionality
Interpolation and Basis Functions
5. Support Vector Machines
Optimal Separation
Kernels
6. Learning With Trees
Using Decision Trees
Constructing Decision Trees
Classification And Regression Trees (CART)
Classification Example
7. Decision by Committee: Ensemble Learning
Boosting
Bagging
Different Ways to Combine Classifiers
8. Probability and Learning
Turning Data into Probabilities
Some Basic Statistics
Gaussian Mixture Models
Nearest Neighbour Methods
9. Unsupervised Learning
The k-Means Algorithm
Vector Quantisation
The Self-Organising Feature Map
10. Dimensionality Reduction
Linear Discriminant Analysis (LDA)
Principal Components Analysis (PCA)
Factor Analysis
Independent Components Analysis (ICA)
Locally Linear Embedding
Isomap
11. Optimisation and Search
Going Downhill
Least-Squares Optimisation
Conjugate Gradients
Search: Three Basic Approaches
Exploitation and Exploration
Simulated Annealing
12. Evolutionary Learning
The Genetic Algorithm (GA)
Generating Offspring: Genetic Operators
Using Genetic Algorithms
Genetic Programming
Combining Sampling with Evolutionary Learning 13. Reinforcement Learning
Overview
Example: Getting Lost
Markov Decision Processes
Values
Back On Holiday: Using Reinforcement Learning
The Difference Between Sarsa and Q-Learning
Uses of Reinforcement Learning
14. Markov Chain Monte Carlo (MCMC) Methods
Sampling
Monte Carlo or Bust
The Proposal Distribution
Markov Chain Monte Carlo
15. Graphical Models
Bayesian Networks
Markov Random Fields
Hidden Markov Models (HMM)
Tracking Methods
16. Python
Installing Python and Other Packages
Getting Started
Code Basics
Using NumPy and Matplotlib
Index PROYECTO: Robot que aprende a jugar juegos de mesa
