Machine Learning: The Art and Science of Algorithms that Make Sense of Data [PDF]

Table of contents

1- The ingredients of machine learning.

• Tasks: the problems that can be solved with machine learning.
• Looking for structure.
• Models: the output of machine learning.
• Geometric models.
• Probabilistic models.
• Logical models.
• Grouping and grading.
• Features: the workhorses of machine learning.
• Many uses of features.
• Feature construction and transformation.

2- Binary classification and related tasks.

• Classification.
• Assessing classification performance.
• Visualising classification performance.
• Scoring and ranking.
• Assessing and visualizing ranking performance.
• Tuning rankers.
• Class probability estimation.
• Assessing class probability estimates.

3- Beyond binary classification.

• Handling more than two classes.
• Multi-class classification.
• Multi-class scores and probabilities.
• Regression.
• Unsupervised and descriptive learning.
• Predictive and descriptive clustering.
• Other descriptive models.

4- Tree models.

• Decision trees.
• Ranking and probability estimation trees.
• Sensitivity to skewed class distributions.
• Tree learning as variance reduction.
• Regression trees.
• Clustering trees.

5- Rule models.

• Learning ordered rule lists.
• Rule lists for ranking and probability estimation.
• Learning unordered rule sets.
• Rule sets for ranking and probability estimation.
• Descriptive rule learning.
• Rule learning for subgroup discovery.
• Association rule mining.

6- Linear models.

• The least-squares method.
• Multivariate linear regression.
• The perceptron: a heuristic learning algorithm for linear classifiers.
• Support vector machines.
• Soft margin SVM.
• Obtaining probabilities from linear classifiers.