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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Shareable Certificate
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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 stars92.61%
- 4 stars6.13%
- 3 stars0.56%
- 2 stars0.34%
- 1 star0.34%
TOP REVIEWS FROM 機器學習基石上 (MACHINE LEARNING FOUNDATIONS)---MATHEMATICAL FOUNDATIONS
by JSApr 17, 2020
以比較數學理論的角度解析機器學習，並且作為立論導入機器學習的領域，數學的部分真的蠻有難度，需要去思考一下，但是整體來說對於機器學習的概念有非常大的幫助，甚至可以藉由這些理論在一些案例中進行修正，非常有幫助。
by BBAug 25, 2019
Good course, which help me understand mathematical foundations about machine learning!
by LLMar 24, 2018
it gives me a different view of ml,it's a really rigorous course. Thanks
by SNNov 15, 2018
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！
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