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 StumpPerceptronMachine LearningVc 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
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 TTMar 3, 2018
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.
by ZLAug 26, 2020
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。
by DAApr 25, 2020
Really theoretical and challenging for somebody with little math like me. But very interesting to learn! Looking forward to the 2nd part.
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