《Machine Learning（Tom M. Mitchell）》读书笔记——2、第一章_machine learning by tom m. mitchell-优快云博客

本文是《Machine Learning》一书的读书笔记，主要介绍了机器学习的基本定义、核心要素和选择，如任务类型、性能衡量标准、训练经验、目标函数、函数表示和学习算法。通过国际跳棋学习问题探讨了如何选择训练经验、目标函数，以及如何用线性组合来表示目标函数，并提出了最小子均误差（LMS）算法进行权重调整。

1. Introduction (about machine learning)

2. Concept Learning and the General-to-Specific Ordering

3. Decision Tree Learning

4. Artificial Neural Networks

5. Evaluating Hypotheses

6. Bayesian Learning

7. Computational Learning Theory

8. Instance-Based Learning

9. Genetic Algorithms

10. Learning Sets of Rules

11. Analytical Learning

12. Combining Inductive and Analytical Learning

13. Reinforcement Learning

1. Introduction (about machine learning)

Several recent applications of machine learning are summarized as following:

Machine learning is inherently a multidisciplinary field. The key ideas from different fields that impact the field of machine learning are as following:

Definition: A computer program is said to LEARN from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with expericence E.

In general, to have a well-defined learning problem, we must identity these features: the class of tasks, the measure of performance to be inproved, and the source of experience.

A checkers learning problem:

Task T: playing checkers 0 Performance measure

P: percent of games won in the world tournament

Training experience E: games played against itself

Designing a machine learning approach involves a number of design choices, including choosing the type of training experience, the target function to be learned, a representation for this target function. and an algorithm for learning the target function from training examples.

Choosing the Training Experience:

The first design choice we face is to choose the type of training experience from which our system will learn. One key attribute is whether the training experience provides direct or indirect feedback regarding the choices made by the performance system. Learning from direct training feedback is typically easier than learning from indirect feedback.

A second important attribute of the training experience is the degree to which the learner controls the sequence of training examples.

A third important attri