Ⅰ. Machine Learning   Andrew Ng

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametricn-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (biasariance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Ⅱ. Neural Networks for Machine Learning   Geoffrey Hinton

Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.

This YouTube video gives examples of the kind of material that will be in the course, but the course will present this material at a much gentler rate and with more examples.

Ⅲ. 机器学习 余凯，张潼

Kai Yu, a deputy engeering director of Baidu, managing the company's multimedia department.

Tong Zhang， Professor in Department of Statistics, Rugers University.

   今天，如果你从事互联网搜索，在线广告，用户行为分析，图像识别，自然语言理解，或者生物信息学，智能机器人，金融预测，那么有一门核心课程你必须深入了解，那就是－机器学习(Machine Learning)。作为人工智能的核心内容，机器学习致力于开发智能的计算机算法从历史经验数据中学习出有用的模型，从而对未知数据或事件做预测。作为一门前沿学科，它结合了计算机算法，概率论，统计学，脑神经科学，控制论，心理学，和优化理论等多方面知识。

   两位授课者在机器学习领域享有国际声誉，不仅各自在世界顶级杂志和会议上发表了上百篇学术论文，而且都在著名高科技公司积累了多年左右的工作经验。通过这门课程，学生将系统掌握习机器学习的基本知识，理论，和算法，还将通过一些实例领略其在应用中发挥的巨大作用。Course Videos, Course Slides

前两个课程都是Coursera上面的课程，可以注册一个用户，免费参与课程的学习。第一个课程的影响力很大，课程大概在今年4月22日开始新的课程，该课程持续10周。

第二个课程，推荐的主要理由是因为它由Deep learning大牛Hiton主讲的，我也才看几集，还是很值得学习的课程。

后面一个课程是国内的龙星计划课程，讲了机器学习的基本理论，同时，也引入了机器学习中比较前沿的研究课题。总共19集，每集大约45分钟（中文课程）。

The main content is copied from:

1. https://www.coursera.org/course/ml

2. https://www.coursera.org/course/neuralnets

3. http://bigeye.au.tsinghua.edu.cn/DragonStar2012/index.html