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。]
機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
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機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
Instructor: 林軒田
16,924 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.9
(328 reviews)
Intermediate level
Some related experience required
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
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Assessments
2 assignments
Taught in Chinese (Traditional)
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There are 8 modules in this course
weight vector for linear hypotheses and squared error instantly calculated by analytic solution
What's included
4 videos4 readings
gradient descent on cross-entropy error to get good logistic hypothesis
What's included
4 videos
binary classification via (logistic) regression; multiclass classification via OVA/OVO decomposition
What's included
4 videos
nonlinear model via nonlinear feature transform+linear model with price of model complexity
What's included
4 videos1 assignment
overfitting happens with excessive power, stochastic/deterministic noise and limited data
What's included
4 videos
minimize augmented error, where the added regularizer effectively limits model complexity
What's included
4 videos
(crossly) reserve validation data to simulate testing procedure for model selection
What's included
4 videos
be aware of model complexity, data goodness and your professionalism
What's included
4 videos1 assignment
Instructor
Instructor ratings
4.8 (23 ratings)Offered by
Recommended if you're interested in Machine Learning
National Taiwan University
Fractal Analytics
Politecnico di Milano
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4.9
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