Table of Contents
Part I: Introduction to data mining
Chapter 1. What’s it all about?
1.1 Data Mining and Machine Learning
1.2 Simple Examples: The Weather Problem and Others
1.5 Machine Learning and Statistics
1.8 Further Reading and Bibliographic Notes
Chapter 2. Input: Concepts, instances, attributes
2.5 Further Reading and Bibliographic Notes
Chapter 3. Output: Knowledge representation
3.5 Instance-Based Representation
3.7 Further Reading and Bibliographic Notes
Chapter 4. Algorithms: The basic methods
4.1 Inferring Rudimentary Rules
4.2 Simple Probabilistic Modeling
4.3 Divide-and-Conquer: Constructing Decision Trees
4.4 Covering Algorithms: Constructing Rules
4.10 Further Reading and Bibliographic Notes
Chapter 5. Credibility: Evaluating what’s been learned
5.6 Comparing Data Mining Schemes
5.9 Evaluating Numeric Prediction
5.11 Applying the MDL Principle to Clustering
Part II: More advanced machine learning schemes
Part II. More advanced machine learning schemes
Chapter 7. Extending instance-based and linear models
7.3 Numeric Prediction With Local Linear Models
Chapter 8. Data transformations
8.2 Discretizing Numeric Attributes
8.6 Transforming Multiple Classes to Binary Ones
8.7 Calibrating Class Probabilities
8.8 Further Reading and Bibliographic Notes
Chapter 9. Probabilistic methods
9.3 Clustering and Probability Density Estimation
9.5 Bayesian Estimation and Prediction
9.6 Graphical Models and Factor Graphs
9.7 Conditional Probability Models
9.8 Sequential and Temporal Models
9.9 Further Reading and Bibliographic Notes
10.1 Deep Feedforward Networks
10.2 Training and Evaluating Deep Networks
10.3 Convolutional Neural Networks
10.6 Recurrent Neural Networks
10.7 Further Reading and Bibliographic Notes
10.8 Deep Learning Software and Network Implementations
Chapter 11. Beyond supervised and unsupervised learning
11.3 Further Reading and Bibliographic Notes
12.1 Combining Multiple Models
12.8 Further Reading and Bibliographic Notes
Chapter 13. Moving on: applications and beyond
13.1 Applying Machine Learning
13.2 Learning From Massive Datasets
13.4 Incorporating Domain Knowledge
Appendix A. Theoretical foundations
A.2 Fundamental Elements of Probabilistic Methods
Appendix B. The WEKA workbench
B.2 The package management system