 台湾大学推出的一系列慕课水平都很高，这门“机器学习基础”介绍各领域中的机器学习使用者都应该知道的基础算法、理论及实用工具。主讲教授将复杂枯燥的理论讲解的通俗易懂，又是中文课，很适合国内的小伙伴选修，9月23日开课 去报名本课可以和斯坦福大学另一门经典的机器学习课程做搭配，前者偏重理论，后者偏重算法： 课程概述欢迎大家！这门课将采用英文投影片配合中文的教学讲解，我们希望能借这次中文教学的机会，将机器学习介绍给更多华人世界的初学者。课程中使用的英文投影片不会使用到艰深的英文，如果你能了解以下两段的课程简介，你应该也可以了解课程所使用的英文投影片。 Machine learning is an exciting field with lots of applications in engineering, science, finance, and commerce. It is also a very dynamic field, where many new techniques are being designed every day, and the hot techniques and theories at times can rise and disappear rapidly. Thus, users of machine learning from other fields often face the problem of choosing or using the techniques properly. In this course, we emphasize the necessary fundamentals that give any student of machine learning a solid foundation, and enable him or her to exploit current techniques properly, explore further techniques and theories, or perhaps to contribute their own in the future. The course roughly corresponds to the first half-semester of the National Taiwan University course “Machine Learning”, and the second half-semester is expected to be on Coursera under the name “Machine Learning Techniques” soon in the future. Based on five years of teaching this popular course successfully (including winning the most prestigious teaching award of National Taiwan University) and discussing with many other scholars actively, the instructor chooses to focus on what he believes to be the core topics that every student of the subject should know. The students shall enjoy a story-like flow moving from “When Can Machines Learn” to “Why”, “How” and beyond. 课程大纲以下的每个小项目对应约一小时的在线课程 为什麽机器可以学习？ 机器可以怎麽样学习？ 机器可以怎麽样学得更好？ 先修知识我们希望修课的同学对于基本的微分、向量与矩阵运算、及机率的工具有所了解。有些作业会需要写作或执行一些程式，所以我们建议修课的同学能在你所熟悉的平台上有一些编程背景。 参考资料虽然这门课的录影课程及投影片应该足以帮大家了解所有的内容，我们推荐有兴趣的同学们阅读 Learning from Data一书，该书包含了本课程中所介绍的大部份的内容。 授课形式这门课主要以线上录影课程及其中的小测验组成，每两週我们会有另外的作业练习。 常见问题解答我在完成课程后，是否能得到“修业合格证明”? 修习此课需要哪些设备/资源？ 我在此课程可以有什么收获？ |
