Informações sobre o curso: 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。]
Desenvolvido por: Universidade Nacional de Taiwan
Ministrado por: Hsuan-Tien Lin, 林軒田, 教授 (Professor)
資訊工程學系 (Computer Science and Information Engineering)
| Nível | Beginner |
| Idioma | Chinese (Traditional) |
| Requisitos de hardware | 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 |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
Programa
SEMANA 1
第一講：The Learning Problem
what machine learning is and its connection to applications and other fields
5 vídeos, 4 leituras
- Vídeo: Course Introduction
- Vídeo: What is Machine Learning
- Vídeo: Applications of Machine Learning
- Vídeo: Components of Machine Learning
- Vídeo: 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
SEMANA 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 vídeos
- Vídeo: Perceptron Hypothesis Set
- Vídeo: Perceptron Learning Algorithm (PLA)
- Vídeo: Guarantee of PLA
- Vídeo: Non-Separable Data
SEMANA 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 vídeos
- Vídeo: Learning with Different Output Space
- Vídeo: Learning with Different Data Label
- Vídeo: Learning with Different Protocol
- Vídeo: Learning with Different Input Space
SEMANA 4
第四講：Feasibility of Learning
learning can be "probably approximately correct" when given enough statistical data and finite number of hypotheses
4 vídeos
- Vídeo: Learning is Impossible?
- Vídeo: Probability to the Rescue
- Vídeo: Connection to Learning
- Vídeo: Connection to Real Learning
Nota atribuída: 作業一
SEMANA 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 vídeos
- Vídeo: Recap and Preview
- Vídeo: Effective Number of Lines
- Vídeo: Effective Number of Hypotheses
- Vídeo: Break Point
SEMANA 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 vídeos
- Vídeo: Restriction of Break Point
- Vídeo: Bounding Function: Basic Cases
- Vídeo: Bounding Function: Inductive Cases
- Vídeo: A Pictorial Proof
SEMANA 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 vídeos
- Vídeo: Definition of VC Dimension
- Vídeo: VC Dimension of Perceptrons
- Vídeo: Physical Intuition of VC Dimension
- Vídeo: Interpreting VC Dimension
SEMANA 8
第八講: Noise and Error
learning can still happen within a noisy environment and different error measures
4 vídeos
- Vídeo: Noise and Probabilistic Target
- Vídeo: Error Measure
- Vídeo: Algorithmic Error Measure
- Vídeo: Weighted Classification
Nota atribuída: 作業二
Perguntas frequentes
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Coursework
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Desenvolvedores
Universidade Nacional de Taiwan
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/
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Best course for the learners with some concepts of machine learning