机器学习当前研究进展.ppt

A Few Useful Things to Knowabout Machine Learning Author’s Introduction Pedro Domingos /course/machlearning Outline Introduction Twelve key lessons Conclusions Outline Introduction Twelve key lessons Conclusions Introduction Machine Learning A few quotes “A breakthrough in machine learning would be worth ten Microsofts” (Bill Gates, Chairman, Microsoft) Machine learning is the hot new thing” (John Hennessy, President, Stanford) “Machine learning is today’s discontinuity” (Jerry Yang, Founder, Yahoo) “Machine learning today is one of the hottest aspects of computer science” (Steve Ballmer, CEO, Microsoft) Introduction Machine Learning Traditional Programming Machine Learning Introduction Types of Learning Supervised (inductive) learning Training data includes desired outputs Unsupervised learning Training data does not include desired outputs Semi-supervised learning Training data includes a few desired outputs Reinforcement learning Rewards from sequence of actions Introduction A recent report from the McKinsey Global Institute, 2011 “machine learning (a.k.a. data mining or predictive analytics) will be the driver of the next big wave of innovation.” Introduction Purpose of this article “Several fine textbooks are available to interested practitioners and researchers. However, much of the ‘folk knowledge’ that is needed to successfully develop machine learning applications is not readily available in them.” “As a result, many machine learning projects take much longer than necessary or wind up producing less than ideal results. Yet much of this folk knowledge is fairly easy to communicate.” Outline Introduction Twelve key lessons Conclusions Outline Introduction Twelve key lessons 1.Learning = representation + evaluation + optimization 2. It’s generalization that counts （泛化） 3. Data alone is not enough （先验知识） 4. Overfitting has many faces （过拟合） 5. Intuition fails in high dimensions （高维） 6. Theoretical guarantees are not what they seem（理论