Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. This first course of the two would focus more on mathematical tools, and the other course would focus more on algorithmic tools. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。我們的兩項姊妹課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。本課程將較為著重數學類的工具，而另一課程將較為著重方法類的工具。]
IW
Thank Prof. Lin and coursera for providing me with the platform and courses to make me better. Last but not least, the quiz is quite difficult...
LL
This course give a theoretical analysis of machine learning,though there is not much introduction of algorithm in detail,but this helped me open a new door of machine learning.
HL
This is a very nice courses. But I think the test is super hard for some students.
EC
Base on very fundamental concepts of the Machine Learning.
FF
Best course for the learners with some concepts of machine learning
AW
Some homework problems are too hard to solve... But I do enjoy studying ML.
BB
Good course, which help me understand mathematical foundations about machine learning!
CC
基石上是很基础的一些知识，VC等等，不过我觉得作为想搞应用的人来说，这些底层数学原理不用学的那么详细，理解含义和思想即可，但是知道这些还是必须的！
ZL
林老师这部分课程内容偏理论，对数理基础有一定的要求。每次作业题需要认真思考，作业后面的编程部分也能锻炼实践能力，进一步巩固所学习的理论知识。总体来说质量不错，不过希望老师以后能以后对课件里面的问题进行总结的时候可以不用太story-like，或者说更简练一点，我认为这样可以有助于学习暂停视频并好好理解。
MC
The speaker explains the ML in very clear and easier to understand way. I believe everyone can understand if he/she follow the course.
TT
I am very grateful to the teacher for bring me to the world of Machine Learing. I am new in the field. I will try my best to learn the basic knowledge of ML.
JG
許多名詞似乎是自創新詞，但都能很好地描述ML的理論課程的統計很吃重，難度的分配有些不均勻整體來說是非常適合有數學底子學生的扎實入門課程
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The speaker explains the ML in very clear and easier to understand way. I believe everyone can understand if he/she follow the course.
Lin is a very funny teacher.The course is really valuable.i will recap this course many times.thanks teacher lin again.thks
完全不推荐！这门课讲的全是 机器学习中的前提与假设，并且很多概念被老师复杂化了，可能老师拐弯抹角的讲某些概念的本意是为了学生更好的理解，但是真的讲得更复杂了。除非对机器学习的发展历史有执念、或者是专门深入研究机器学习的人，否则不推荐这门课，更推荐吴恩达和李宏毅的课。PS：本人专业硕士，方向机器学习，这门课中的很多东西我都用不上！ 可能有志于学术方面的学硕或博士用得上。
机器学习的数学和统计学基础本是让人望而生畏的部分，但林老师的讲解深入浅出，循序渐进，在有限的时间内让我领略了机器学习背后的原理，为后续学习机器学习算法增加了信心，非常感谢林老师的课程！
以前曾在台大上过课，对台大老师授课循序渐进的思路十分钦佩。这绝对是一门能够吸引学生全神贯注听讲的机器学习课程。没有复杂的推导，有生动的例程和老师细心的讲解，可以推测老师一定是花了许多心血经营这门课程。
林老师知道怎样把一个什么都不懂的学生教会，让他听懂。从这一点看，老师真的非常用心地在准备。在此对林老师的付出表示由衷的感谢！那句话这么说来着 douxia！
講得很清晰，而且從「演算法為什麼可以學習」切入，是其他課程比較少聽到的講解角度，很推薦
This course & the following course cover ML theory, which involve many math & theoretical derivations, and are thus more difficult than Andrew Ng's ML course. I feel this course is suitable for ML researchers, not for ML beginner. Besides, the quizzes are more challenging than the lectures. In order to tackle the quizzes, very often you need watch the lectures several times to get a correct understanding of the content.
基石上是很基础的一些知识，VC等等，不过我觉得作为想搞应用的人来说，这些底层数学原理不用学的那么详细，理解含义和思想即可，但是知道这些还是必须的！
这个课程对于原理的解释比较细致，能知其然也知其所以然，很棒！
理论性很强，理性认识和感性认识结合
This course give a theoretical analysis of machine learning,though there is not much introduction of algorithm in detail,but this helped me open a new door of machine learning.
Other course only tells me how to use ML but this course tells me to how to deep-in understand ML foundations. I am very grateful to the teacher, 林軒田. Thank you soooo much.
I am very grateful to the teacher for bring me to the world of Machine Learing. I am new in the field. I will try my best to learn the basic knowledge of ML.
Though I know many machine algorithms, but I've never thought about WHY machine can learn. This course let me know (to some extent) the fundamental issue.
Thank Prof. Lin and coursera for providing me with the platform and courses to make me better. Last but not least, the quiz is quite difficult...
wonderful class !
the teacher from NTU is quite humorous and gives class in an interesting and simple way! highly recommended!
Hard for mathmatical reasons, which made a solid fundation. Should recall or relearn math if you really wanna do sth on ML.
from the introduction to the instructor Avery thing is awesome I really enjoyed Avery moment I had during this course
Theoretically explained why machine learning is feasible. The content is a little bit hard to understand.