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。]
weight vector for linear hypotheses and squared error instantly calculated by analytic solution
Das ist alles enthalten
4 Videos4 Lektüren
Infos zu Modulinhalt anzeigen
4 Videos•Insgesamt 62 Minuten
Linear Regression Problem•10 Minuten
Linear Regression Algorithm•20 Minuten
Generalization Issue•21 Minuten
Linear Regression for Binary Classification•11 Minuten
4 Lektüren•Insgesamt 31 Minuten
NTU MOOC 課程問題詢問與回報機制•1 Minute
課程大綱•10 Minuten
課程形式及評分標準•10 Minuten
延伸閱讀•10 Minuten
第十講:Logistic Regression
Modul 2•1 Stunde abzuschließen
Moduldetails
gradient descent on cross-entropy error
to get good logistic hypothesis
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4 Videos
Infos zu Modulinhalt anzeigen
4 Videos•Insgesamt 65 Minuten
Logistic Regression Problem•15 Minuten
Logistic Regression Error•16 Minuten
Gradient of Logistic Regression Error•16 Minuten
Gradient Descent•19 Minuten
第十一講:Linear Models for Classification
Modul 3•1 Stunde abzuschließen
Moduldetails
binary classification via (logistic) regression; multiclass classification via OVA/OVO decomposition
Das ist alles enthalten
4 Videos
Infos zu Modulinhalt anzeigen
4 Videos•Insgesamt 59 Minuten
Linear Models for Binary Classification•22 Minuten
Stochastic Gradient Descent•12 Minuten
Multiclass via Logistic Regression•14 Minuten
Multiclass via Binary Classification•12 Minuten
第十二講:Nonlinear Transformation
Modul 4•2 Stunden abzuschließen
Moduldetails
nonlinear model via nonlinear feature transform+linear model with price of model complexity
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4 Videos1 Aufgabe
Infos zu Modulinhalt anzeigen
4 Videos•Insgesamt 59 Minuten
Quadratic Hypothesis•24 Minuten
Nonlinear Transform•10 Minuten
Price of Nonlinear Transform•16 Minuten
Structured Hypothesis Sets•10 Minuten
1 Aufgabe•Insgesamt 40 Minuten
作業三•40 Minuten
第十三講:Hazard of Overfitting
Modul 5•1 Stunde abzuschließen
Moduldetails
overfitting happens with excessive power, stochastic/deterministic noise and limited data
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4 Videos
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4 Videos•Insgesamt 49 Minuten
What is Overfitting?•11 Minuten
The Role of Noise and Data Size•14 Minuten
Deterministic Noise•14 Minuten
Dealing with Overfitting•11 Minuten
第十四講:Regularization
Modul 6•1 Stunde abzuschließen
Moduldetails
minimize augmented error, where the added regularizer effectively limits model complexity
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4 Videos
Infos zu Modulinhalt anzeigen
4 Videos•Insgesamt 65 Minuten
Regularized Hypothesis Set•19 Minuten
Weight Decay Regularization•24 Minuten
Regularization and VC Theory•8 Minuten
General Regularizers•13 Minuten
第十五講:Validation
Modul 7•1 Stunde abzuschließen
Moduldetails
(crossly) reserve validation data to simulate testing procedure for model selection
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4 Videos
Infos zu Modulinhalt anzeigen
4 Videos•Insgesamt 56 Minuten
Model Selection Problem•16 Minuten
Validation•13 Minuten
Leave-One-Out Cross Validation•16 Minuten
V-Fold Cross Validation•11 Minuten
第十六講:Three Learning Principles
Modul 8•1 Stunde abzuschließen
Moduldetails
be aware of model complexity, data goodness and your professionalism
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4 Videos1 Aufgabe
Infos zu Modulinhalt anzeigen
4 Videos•Insgesamt 43 Minuten
Occam's Razor•10 Minuten
Sampling Bias•12 Minuten
Data Snooping•12 Minuten
Power of Three•9 Minuten
1 Aufgabe•Insgesamt 30 Minuten
作業四•30 Minuten
Dozent
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Wir haben alle Lernenden um Feedback zu unseren Dozenten gebeten, ausgehend von der Qualität ihres Unterrichtsstils.
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.
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