About this course: 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。]
Created by: National Taiwan University
Taught by: 林軒田, 教授 (Professor)
資訊工程學系 (Department of Computer Science and Information Engineering)
| Level | Beginner |
| Language | Chinese (Traditional) |
| Hardware Req | 需要一般個人電腦或同等級的計算資源以撰寫程式執行作業 |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
第一講：The Learning Problem
what machine learning is and its connection to applications and other fields
5 videos, 5 readings
- Reading: NTU MOOC 課程問題詢問與回報機制
- Video: What is Machine Learning
- Video: Applications of Machine Learning
- Video: Components of Machine Learning
- Video: Machine Learning and Other Fields
- Reading: 課程大綱
- Reading: 課程形式及評分標準
- Reading: 延伸閱讀
- Reading: homework 0
WEEK 2
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
4 videos
- Video: Perceptron Learning Algorithm (PLA)
- Video: Guarantee of PLA
- Video: Non-Separable Data
WEEK 3
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
4 videos
- Video: Learning with Different Data Label
- Video: Learning with Different Protocol
- Video: Learning with Different Input Space
WEEK 4
第四講：Feasibility of Learning
learning can be "probably approximately correct" when given enough statistical data and finite number of hypotheses
4 videos
- Video: Probability to the Rescue
- Video: Connection to Learning
- Video: Connection to Real Learning
Graded: 作業一
WEEK 5
第五講：Training versus Testing
what we pay in choosing hypotheses during training: the growth function for representing effective number of choices
4 videos
- Video: Effective Number of Lines
- Video: Effective Number of Hypotheses
- Video: Break Point
WEEK 6
第六講: Theory of Generalization
test error can approximate training error if there is enough data and growth function does not grow too fast
4 videos
- Video: Bounding Function: Basic Cases
- Video: Bounding Function: Inductive Cases
- Video: A Pictorial Proof
WEEK 7
第七講: The VC Dimension
learning happens if there is finite model complexity (called VC dimension), enough data, and low training error
4 videos
- Video: VC Dimension of Perceptrons
- Video: Physical Intuition of VC Dimension
- Video: Interpreting VC Dimension
WEEK 8
第八講: Noise and Error
learning can still happen within a noisy environment and different error measures
4 videos
- Video: Error Measure
- Video: Algorithmic Error Measure
- Video: Weighted Classification
Graded: 作業二
FAQs
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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. More courses information, the official Facebook Page: https://www.facebook.com/ntumooc2017/
Ratings and Reviews
讲的很好，但是真的是非常基石了，前面8讲只讲了一个PLA算法也是醉了
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！
很不错，要是细节推导可以再讲多一点就更完美了，谢谢林老师的付出！
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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！