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Voltar para 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations

Comentários e feedback de alunos de 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations da instituição Universidade Nacional de Taiwan

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Sobre o curso

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。]...

Melhores avaliações

LL

23 de jun de 2018

This course give a theoretical analysis of machine learning,though there is not much introduction of algorithm in detail,but this helped me open a new door of machine learning.

TT

3 de mar de 2018

I am very grateful to the teacher for bring me to the world of Machine Learing. I am new in the field. I will try my best to learn the basic knowledge of ML.

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1 — 25 de 159 Avaliações para o 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations

por Ming-Chieh C

18 de fev de 2018

por 敖勇刚

20 de out de 2017

por Sidney N

16 de nov de 2018

por Daya_Jin

11 de jun de 2018

por 林Lin智海Chihhai

12 de jun de 2018

por 王中兴

17 de mai de 2018

por Jack W

6 de fev de 2022

por ChaoLin

24 de out de 2018

por 鄭祐銓

31 de jan de 2021

por 陈千奔

11 de mai de 2019

por cyxin

1 de jun de 2018

por LiXiang

23 de jun de 2018

por Hong H

9 de fev de 2019

por t_xinxishijie

4 de mar de 2018

por zhaoone

15 de set de 2019

por 王逸辰

19 de nov de 2017

por 黄东晓

13 de jan de 2018

por 杨之龙

24 de jan de 2018

por Minglu Z

28 de ago de 2018

por 石傑宇

18 de abr de 2020

por Jiqing H

17 de set de 2017

por Zhike C

9 de jul de 2020

por Zhi-Sheng C

26 de set de 2022

por Bai

26 de ago de 2019

por ShaohuaWang

23 de jul de 2017