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首页machine learning the art and science of algorithms that make sense of data PPT

machine learning Tasks: the problems that can be solved with machine learning Models: the output of machine learning Features: the workhorses of machine learning

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Table of contents I

1

The ingredients of machine learning

Tasks: the problems that can be solved with machine learning

Models: the output of machine learning

Features: the workhorses of machine learning

2

Binary classiﬁcation and related tasks

Classiﬁcation

Scoring and ranking

Class probability estimation

3

Beyond binary classiﬁcation

Handling more than two classes

Regression

Unsupervised and descriptive learning

Peter Flach (University of Bristol) Machine Learning: Making Sense of Data August 25, 2012 2 / 349

Table of contents II

4

Concept learning

The hypothesis space

Paths through the hypothesis space

Beyond conjunctive concepts

5

Tree models

Decision trees

Ranking and probability estimation trees

Tree learning as variance reduction

6

Rule models

Learning ordered rule lists

Learning unordered rule sets

Descriptive rule learning

First-order rule learning

Peter Flach (University of Bristol) Machine Learning: Making Sense of Data August 25, 2012 3 / 349

Table of contents III

7

Linear models

The least-squares method

The perceptron: a heuristic learning algorithm for linear classiﬁers

Support vector machines

Obtaining probabilities from linear classiﬁers

Going beyond linearity with kernel methods

8

Distance-based models

Neighbours and exemplars

Nearest-neighbour classiﬁcation

Distance-based clustering

Hierarchical clustering

Peter Flach (University of Bristol) Machine Learning: Making Sense of Data August 25, 2012 4 / 349

Table of contents IV

9

Probabilistic models

The normal distribution and its geometric interpretations

Probabilistic models for categorical data

Discriminative learning by optimising conditional likelihood

Probabilistic models with hidden variables

10

Features

Kinds of feature

Feature transformations

Feature construction and selection

Peter Flach (University of Bristol) Machine Learning: Making Sense of Data August 25, 2012 5 / 349

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