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。]
Offered By
機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
National Taiwan UniversityAbout this Course
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Beginner Level
Approx. 10 hours to complete
Chinese (Traditional)
Subtitles: Chinese (Traditional)
Skills you will gain
- Decision Stump
- Perceptron
- Machine Learning
- Vc Dimension
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Beginner Level
Approx. 10 hours to complete
Chinese (Traditional)
Subtitles: Chinese (Traditional)
Offered by
National Taiwan University
We firmly believe that open access to learning is a powerful socioeconomic equalizer. NTU is especially delighted to join other world-class universities on Coursera and to offer quality university courses to the Chinese-speaking population. We hope to transform the rich rewards of learning from a limited commodity to an experience available to all.
Syllabus - What you will learn from this course
2 hours to complete
第一講：The Learning Problem
what machine learning is and its connection to applications and other fields
2 hours to complete
5 videos (Total 70 min), 5 readings
1 hour to complete
第二講：Learning to Answer Yes/No
your first learning algorithm (and the world's first!) that "draws the line" between yes and no by adaptively searching for a good line based on data
1 hour to complete
4 videos (Total 61 min)
1 hour to complete
第三講：Types of Learning
learning comes with many possibilities in different applications, with our focus being binary classification or regression from a batch of supervised data with concrete features
1 hour to complete
4 videos (Total 61 min)
1 hour to complete
第四講：Feasibility of Learning
learning can be "probably approximately correct" when given enough statistical data and finite number of hypotheses
1 hour to complete
4 videos (Total 60 min)
Reviews
- 5 stars92.88%
- 4 stars5.96%
- 3 stars0.57%
- 2 stars0.22%
- 1 star0.34%
TOP REVIEWS FROM 機器學習基石上 (MACHINE LEARNING FOUNDATIONS)---MATHEMATICAL FOUNDATIONS
by LLJun 23, 2018
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.
by GGOct 19, 2017
Lin is a very funny teacher.The course is really valuable.i will recap this course many times.thanks teacher lin again.thks
by CCOct 24, 2018
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！
by JGAug 17, 2017
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\n\n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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