Об этом курсе: 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。]
Автор: National Taiwan University
Преподаватели: 林軒田, 教授 (Professor)
資訊工程學系 (Computer Science and Information Engineering)
| Уровень | Учащийся среднего уровня |
| Язык | Chinese (Traditional) |
| Требования к аппаратным средствам | 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 |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
第九講: Linear Regression
weight vector for linear hypotheses and squared error instantly calculated by analytic solution
4 видео, 4 материала для самостоятельного изучения
- Reading: NTU MOOC 課程問題詢問與回報機制
- Видео: Linear Regression Algorithm
- Видео: Generalization Issue
- Видео: 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
НЕДЕЛЯ 2
第十講: Logistic Regression
gradient descent on cross-entropy error
to get good logistic hypothesis
4 видео
- Видео: Logistic Regression Error
- Видео: Gradient of Logistic Regression Error
- Видео: Gradient Descent
НЕДЕЛЯ 3
第十一講: Linear Models for Classification
binary classification via (logistic) regression; multiclass classification via OVA/OVO decomposition
4 видео
- Видео: Stochastic Gradient Descent
- Видео: Multiclass via Logistic Regression
- Видео: Multiclass via Binary Classification
НЕДЕЛЯ 4
第十二講: Nonlinear Transformation
nonlinear model via nonlinear feature transform+linear model with price of model complexity
4 видео
- Видео: Nonlinear Transform
- Видео: Price of Nonlinear Transform
- Видео: Structured Hypothesis Sets
Оцениваемый: 作業三
НЕДЕЛЯ 5
第十三講: Hazard of Overfitting
overfitting happens with excessive power, stochastic/deterministic noise and limited data
4 видео
- Видео: The Role of Noise and Data Size
- Видео: Deterministic Noise
- Видео: Dealing with Overfitting
НЕДЕЛЯ 6
第十四講: Regularization
minimize augmented error, where the added regularizer effectively limits model complexity
4 видео
- Видео: Weight Decay Regularization
- Видео: Regularization and VC Theory
- Видео: General Regularizers
НЕДЕЛЯ 7
第十五講: Validation
(crossly) reserve validation data to simulate testing procedure for model selection
4 видео
- Видео: Validation
- Видео: Leave-One-Out Cross Validation
- Видео: V-Fold Cross Validation
НЕДЕЛЯ 8
第十六講: Three Learning Principles
be aware of model complexity, data goodness and your professionalism
4 видео
- Видео: Sampling Bias
- Видео: Data Snooping
- Видео: Power of Three
Оцениваемый: 作業四
Часто задаваемые вопросы
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Авторы
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/
Рейтинги и отзывы
機器學習必學課程
good explaination the foundation of all ML models
good job 希望技法的课也能开呀。
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谢谢老师助教们的付出！