À propos de ce cours : 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 second course of the two would focus more on algorithmic tools, and the other course would focus more on mathematical 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。本課程將較為著重方法類的工具，而另一課程將較為著重數學類的工具。]
Créé par : National Taiwan University
Enseigné par : 林軒田, 教授 (Professor)
資訊工程學系 (Department of Computer Science and Information Engineering)
| Niveau | Intermédiaire |
| Langue | Chinois (traditionnel) |
| Matériel requis | 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 |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
第九講: Linear Regression
weight vector for linear hypotheses and squared error instantly calculated by analytic solution
4 vidéos, 4 lectures
- Reading: NTU MOOC 課程問題詢問與回報機制
- Vidéo: Linear Regression Algorithm
- Vidéo: Generalization Issue
- Vidéo: Linear Regression for Binary Classification
- Reading: 課程大綱
- Reading: 課程形式及評分標準
- Reading: 延伸閱讀
SEMAINE 2
第十講: Logistic Regression
gradient descent on cross-entropy error
to get good logistic hypothesis
4 vidéos
- Vidéo: Logistic Regression Error
- Vidéo: Gradient of Logistic Regression Error
- Vidéo: Gradient Descent
SEMAINE 3
第十一講: Linear Models for Classification
binary classification via (logistic) regression; multiclass classification via OVA/OVO decomposition
4 vidéos
- Vidéo: Stochastic Gradient Descent
- Vidéo: Multiclass via Logistic Regression
- Vidéo: Multiclass via Binary Classification
SEMAINE 4
第十二講: Nonlinear Transformation
nonlinear model via nonlinear feature transform+linear model with price of model complexity
4 vidéos
- Vidéo: Nonlinear Transform
- Vidéo: Price of Nonlinear Transform
- Vidéo: Structured Hypothesis Sets
Noté: 作業三
SEMAINE 5
第十三講: Hazard of Overfitting
overfitting happens with excessive power, stochastic/deterministic noise and limited data
4 vidéos
- Vidéo: The Role of Noise and Data Size
- Vidéo: Deterministic Noise
- Vidéo: Dealing with Overfitting
SEMAINE 6
第十四講: Regularization
minimize augmented error, where the added regularizer effectively limits model complexity
4 vidéos
- Vidéo: Weight Decay Regularization
- Vidéo: Regularization and VC Theory
- Vidéo: General Regularizers
SEMAINE 7
第十五講: Validation
(crossly) reserve validation data to simulate testing procedure for model selection
4 vidéos
- Vidéo: Validation
- Vidéo: Leave-One-Out Cross Validation
- Vidéo: V-Fold Cross Validation
SEMAINE 8
第十六講: Three Learning Principles
be aware of model complexity, data goodness and your professionalism
4 vidéos
- Vidéo: Sampling Bias
- Vidéo: Data Snooping
- Vidéo: Power of Three
Noté: 作業四
FAQ
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Créateurs
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/
Notation et examens
A great course
内容相对上部要容易，涉及到的算法比较基本。
Best ML course I have ever taken!
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J
很好的课程，更加注重算法的理论推导，当然也不乏运用的技巧。之前看过吴恩达老师的机器学习课程，感觉林老师这门课更加的深入，吴恩达老师的课省去了公式的推导，更偏向工程的实践，两门课可以算是相辅相成的。