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。]
機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
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機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
Instructor: 林軒田
49,077 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.9
(933 reviews)
Beginner level
No prior experience required
Flexible schedule
9 hours to complete
Learn at your own pace
96%
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Assessments
2 assignments
Taught in Chinese (Traditional)
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There are 8 modules in this course
what machine learning is and its connection to applications and other fields
What's included
5 videos5 readings
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
What's included
4 videos
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
What's included
4 videos
learning can be "probably approximately correct" when given enough statistical data and finite number of hypotheses
What's included
4 videos1 assignment
what we pay in choosing hypotheses during training: the growth function for representing effective number of choices
What's included
4 videos
test error can approximate training error if there is enough data and growth function does not grow too fast
What's included
4 videos
learning happens if there is finite model complexity (called VC dimension), enough data, and low training error
What's included
4 videos
learning can still happen within a noisy environment and different error measures
What's included
4 videos1 assignment
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4.9 (68 ratings)Offered by
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4.9
933 reviews
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Showing 3 of 933
JH
5
Reviewed on Sep 16, 2017
A great theoretical course in machine learning, and looking for he second part of the math foundation
MC
5
Reviewed on Feb 18, 2018
The speaker explains the ML in very clear and easier to understand way. I believe everyone can understand if he/she follow the course.
CC
5
Reviewed on Oct 5, 2021
Really great theoretical ML course! And it is really hard lol~
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