The Discipline of Machine Learning 笔记

Essay, Machine Learning

Tom M. Mitchell于2006年所写对Machine Learning进行简单介绍的论文。

Defining Questions

Machine Learning的核心问题是：How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?

这里的learn的定义是：with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at Task T, following experience E.

Machine Learning是Computer Science和Statistics的交集，还有一个较弱相关领域就是the study of human and animal learning in Psychology, Neuroscience and related fields，其他对Machine Learning感兴趣的领域如biology、ecomonics和control theory。

State of Machine Learning

Application Successes

  • Speech recognition.
  • Computer vision.
  • Bio-surveillance. 比如detect and track disease outbreak。
  • Robot control. 比如无人飞行器。
  • Accelerating empirical sciences.

Place of Machine Learning within Computer Science

在如下领域Machine Learning已经是best practice。即complex和self-customizing的软件。

  • The application is too complex for people to manually design the algorithm. 比如sensor-base perception tasks (speech recognition, computer vision, etc.)
  • The application requires that the software customize to its operational environment after it is fielded. 比如speech recognition应该用用户数据再训练一遍，推荐系统，自定义垃圾邮件。

Some Current Research Questions

正在被研究(2006年)的前沿问题，例如：

  • Can unlabeled data be helpful for supervised learning?
  • How can we transfer what is learned for one task to improve learning in other related tasks?
  • What is the relationship between different learning algorithms, and which should be used when?
  • For learners that actively collect their own training data, what is the best strategy?
  • To what degree can we have both data privacy and the benefits of data mining?

Longer Term Research Questions

  • Can we build never-ending learners?
    按照进阶顺序不断学习各种课程，课程之间可以融会贯通。 A key research issue here is self-supervised learning and constructing an appropriate graded curriculum.
  • Can machine learning theories and algorithms help explain human learning?
  • Can we design programming languages containing machine learning primitives?
  • Will computer perception merge with machine learning?

Ethical Questions

personal privacy.

Where to Learn More

The top conferences and journals in the field:

  • International Conference on Machine Learning (ICML).
  • Conference on Neural Information Processing Systems (NIPS).
  • Annual Conference on Learning Theory (COLT).
  • Journal of Machine Learning Research (JMLR). This top journal is freely available online at www.jmlr.org.
  • Machine Learning. Published by Springer.