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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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资源评论
- oneinmore2014-08-27Peter Flach关于此书的讲义,近400页
- lawluoxi2014-11-16很全的ppt,给力,感谢分享
- chrisskr2014-11-14PPT很给力啊,全部包含了书本的精华!
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