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
Dozent: 林軒田
48.033 bereits angemeldet
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(921 Bewertungen)
Kompetenzen, die Sie erwerben
- Kategorie: Decision Stump
- Kategorie: Perceptron
- Kategorie: Machine Learning
- Kategorie: Vc Dimension
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In diesem Kurs gibt es 8 Module
what machine learning is and its connection to applications and other fields
Das ist alles enthalten
5 Videos5 Lektüren
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
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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
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4 Videos
learning can be "probably approximately correct" when given enough statistical data and finite number of hypotheses
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4 Videos1 Aufgabe
what we pay in choosing hypotheses during training: the growth function for representing effective number of choices
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4 Videos
test error can approximate training error if there is enough data and growth function does not grow too fast
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4 Videos
learning happens if there is finite model complexity (called VC dimension), enough data, and low training error
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4 Videos
learning can still happen within a noisy environment and different error measures
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4 Videos1 Aufgabe
Dozent
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Bewertungen von Lernenden
921 Bewertungen
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- 3 stars
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Zeigt 3 von 921 an
Geprüft am 25. Mai 2018
hope there are more exercises, some problems seem to be too hard to understand...
Geprüft am 13. Jan. 2021
It is still difficult for a novice especially last 4 lectures.
Geprüft am 16. Sep. 2017
A great theoretical course in machine learning, and looking for he second part of the math foundation
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