Key Information
About the content
Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本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。]
Syllabus
When Can Machines Learn? [何時可以使用機器學習]
-- The Learning Problem [機器學習問題]
-- Learning to Answer Yes/No [二元分類]
-- Types of Learning [各式機器學習問題]
-- Feasibility of Learning [機器學習的可行性]
Why Can Machines Learn? [為什麼機器可以學習]
-- Training versus Testing [訓練與測試]
-- Theory of Generalization [舉一反三的一般化理論]
-- The VC Dimension [VC 維度]
-- Noise and Error [雜訊一錯誤]
How Can Machines Learn? [機器可以怎麼樣學習]
-- Linear Regression [線性迴歸]
-- Linear `Soft' Classification [軟性的線性分類]
-- Linear Classification beyond Yes/No [二元分類以外的分類問題]
-- Nonlinear Transformation [非線性轉換]
How Can Machines Learn Better? [機器可以怎麼樣學得更好]
-- Hazard of Overfitting [過度訓練的危險]
-- Preventing Overfitting I: Regularization [避免過度訓練一：控制調適]
-- Preventing Overfitting II: Validation [避免過度訓練二：自我檢測]
-- Three Learning Principles [三個機器學習的重要原則]
Instructors
- Hsuan-Tien Lin - 資訊工程學系 (Computer Science and Information Engineering)
Content Designer
Platform
Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California.
Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.
