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林軒田
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。]
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What's inside
Syllabus
第一講：The Learning Problem
what machine learning is and its connection to applications and other fields
第二講：Learning to Answer Yes/No
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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Syllabus
第一講：The Learning Problem
what machine learning is and its connection to applications and other fields
第二講：Learning to Answer Yes/No
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
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Taught by 林軒田, whose research influences many of the key topics and tools taught in this course
Suitable for those who want a strong foundation in the fundamentals of machine learning
Covers the basics of machine learning algorithms and theory, making it accessible to beginners
Provides a solid foundation for further exploration in machine learning, by giving an enough background information
Introduces other basic topics in machine learning, like the types of learning and feasibility of learning
Requires some prior background in statistics and probability
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Reviews summary
Rigorous machine learning mathematical foundations
學生們說，這門「機器學習基石上」課程是提供機器學習數學基石的扎實且嚴謹的理論基礎課程。許多學生稱讚林軒田老師的講解深入淺出，能將抽象的數學概念解釋得非常清晰，使學生對VC Dimension和泛化理論有了紮實的理解。課程安排合理，從基礎逐步深入。儘管作業難度較高且具挑戰性，甚至需要花不少時間，但學生普遍認為作業設計巧妙，能有效幫助鞏固概念並從中收穫很大。然而，有部分學生提到，這門課偏重理論推導，缺乏足夠的實務應用和程式碼範例，因此對學習者的數學基礎要求較高。對於非數學背景或僅尋求快速實務技能用於轉職的學習者來說，學習曲線可能較陡峭，感覺離實際工作有些遠。這門課更適合想深入理解機器學習「為什麼」運作或有學術研究方向的學習者。
建議具備一定數學基礎
"對於非數學背景的學生來說，曲線比較陡峭。"
"如果數學基礎不是很好，建議先補習一下相關知識。"
"數學要求比較高，但絕對值得。"
作業有挑戰性，有助於鞏固學習
"作業非常有挑戰性，能幫助鞏固概念。"
"作業難度較大，需要花不少時間。"
"作業設計巧妙，能加深理解。"
"作業雖然難，但完成後收穫很大。"
"作業很燒腦，但確實有效。"
林軒田老師講課深入淺出且嚴謹
"林軒田老師的講解非常深入淺出，理論推導細緻。"
"林老師的數學功底深厚，能把抽象的概念解釋得很清楚。"
"老師講課速度稍快，有些地方需要重複看。"
"林軒田老師的課程一如既往地精彩，理論功底紮實，講解清晰。"
深入闡述機器學習的數學基石
"這門課是我遇過將機器學習數學基石講得最清晰、最嚴謹的課程。"
"對VC Dimension、泛化理論有了紮實的理解。"
"完美地闡述了機器學習的基石理論，VC維、霍夫丁不等式等概念都講得非常清晰。"
"對機器學習的理論基礎進行了系統性的介紹。"
課程理論性強，缺乏實務應用
"課程理論性太強，缺乏實務應用。"
"實際程式碼和應用案例太少。"
"數學推導太多，感覺離實際工作有些遠。"
"可能更適合學術研究方向的人。"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations with these
activities:
阅读《机器学习实战》
Show steps
通过阅读经典著作，深入了解机器学习的基础概念和算法。
View
Machine Learning with Python dialysis combat...
on Amazon
Show steps
-
阅读绪论和第 1-3 章
-
完成第 1-3 章的练习题
学习 TensorFlow 的基础教程
Show steps
通过实践教程，掌握机器学习框架 TensorFlow 的使用，为实践机器学习算法奠定基础。
Browse courses on
TensorFlow
Show steps
-
完成 TensorFlow 官网上的入门教程
-
尝试构建一个简单的线性回归模型
撰写机器学习博客文章
Show steps
通过分享知识，巩固对机器学习概念的理解，并提高沟通能力。
Show steps
-
选择一个感兴趣的机器学习主题
-
研究主题并整理相关材料
-
撰写博客文章，深入浅出地阐述主题
One other activity
Expand to see all activities and additional details
Show all four activities
阅读《Pattern Recognition and Machine Learning》
Show steps
通过阅读高级著作，拓展对机器学习理论和方法的理解。
View
Pattern Recognition and Machine Learning...
on Amazon
Show steps
-
阅读绪论和第 1-4 章
-
完成第 1-4 章的习题
阅读《机器学习实战》
Show steps
通过阅读经典著作，深入了解机器学习的基础概念和算法。
View
Machine Learning with Python dialysis combat...
on Amazon
Show steps
Show steps
Show steps
- 阅读绪论和第 1-3 章
- 完成第 1-3 章的练习题
学习 TensorFlow 的基础教程
Show steps
通过实践教程，掌握机器学习框架 TensorFlow 的使用，为实践机器学习算法奠定基础。
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- 完成 TensorFlow 官网上的入门教程
- 尝试构建一个简单的线性回归模型
撰写机器学习博客文章
Show steps
通过分享知识，巩固对机器学习概念的理解，并提高沟通能力。
Show steps
Show steps
Show steps
- 选择一个感兴趣的机器学习主题
- 研究主题并整理相关材料
- 撰写博客文章，深入浅出地阐述主题
One other activity
Expand to see all activities and additional details
Show all four activities
阅读《Pattern Recognition and Machine Learning》
Show steps
通过阅读高级著作，拓展对机器学习理论和方法的理解。
View
Pattern Recognition and Machine Learning...
on Amazon
Show steps
Show steps
Show steps
- 阅读绪论和第 1-4 章
- 完成第 1-4 章的习题
Career center
Learners who complete 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and maintaining machine learning models. To do so, it is essential to have a strong foundation in the mathematics of machine learning, which Machine Learning Foundations will provide you. In particular, The Learning Problem can help you understand the fundamental concepts of machine learning, and Theory of Generalization can help you understand how to generalize test error from training error.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers are responsible for developing and maintaining artificial intelligence systems. To do so, it is essential to have a strong foundation in machine learning, and Machine Learning Foundations can provide you with that foundation. In particular, The Learning Problem will introduce you to the fundamental concepts of machine learning and its connection to other fields.
Data Scientist
Data Scientist
As a Data Scientist, you would collect, clean, analyze, and interpret data to draw informed conclusions and generate predictions. Machine Learning Foundations can equip you with the mathematical background for building machine learning models that process this data. In particular, The Learning Problem introduces the fundamental concepts of machine learning and shows you how it is used across different fields. The VC Dimension can help you learn about handling noisy environments and its impact on learning.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve complex problems in business and industry, primarily through the use of machine learning algorithms. Machine Learning Foundations can help you will provide you with the mathematical background for building and analyzing these models. In particular, The Learning Problem introduces the fundamental concepts of machine learning, and Feasibility of Learning can help you understand how to assess the feasibility of learning given different parameters.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. To do so, it is helpful to have a strong foundation in statistics and machine learning, which Machine Learning Foundations can provide you. In particular, The Learning Problem and Types of Learning will introduce you to the fundamental concepts of machine learning and the different types of learning algorithms.
Data Engineer
Data Engineer
Data Engineers are responsible for building, deploying, and managing big data systems. To do so, it is indispensable to understand data well, and the mathematical tools of Machine Learning Foundations can help you build that foundation. The Learning Problem and Types of Learning will introduce you to fundamental concepts of machine learning and the different types of learning algorithms. Feasibility of Learning will show you how to evaluate the feasibility of learning based on statistical data and number of hypotheses.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to draw informed conclusions. To do so, it is helpful to have a strong foundation in statistics and machine learning, which Machine Learning Foundations can provide you. In particular, The Learning Problem and Types of Learning will introduce you to the fundamental concepts of machine learning and the different types of learning algorithms.
Actuary
Actuary
Actuaries are responsible for assessing and managing risk. To do so, it is helpful to have a strong foundation in statistics and machine learning, which Machine Learning Foundations can provide you. In particular, The Learning Problem and Types of Learning will introduce you to the fundamental concepts of machine learning and the different types of learning algorithms.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. To do so, it is helpful to have a strong foundation in statistics and machine learning, which Machine Learning Foundations can provide you. In particular, The Learning Problem and Types of Learning will introduce you to the fundamental concepts of machine learning and the different types of learning algorithms.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for assessing and managing risk. To do so, it is helpful to have a strong foundation in statistics and machine learning, which Machine Learning Foundations can provide you. In particular, The Learning Problem and Types of Learning will introduce you to the fundamental concepts of machine learning and the different types of learning algorithms.
Consultant
Consultant
Consultants are responsible for providing advice to businesses on a variety of topics. To do so, it is helpful to have a strong foundation in statistics and machine learning, which Machine Learning Foundations can provide you. In particular, The Learning Problem and Types of Learning will introduce you to the fundamental concepts of machine learning and the different types of learning algorithms.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business data to make recommendations for improvement. To do so, it is helpful to have a strong foundation in statistics and machine learning, which Machine Learning Foundations can provide you. In particular, The Learning Problem and Types of Learning will introduce you to the fundamental concepts of machine learning and the different types of learning algorithms.
Software Engineer
Software Engineer
Software Engineers are responsible for developing and maintaining software systems. To do so, it is helpful to have a strong foundation in the mathematics of machine learning, which Machine Learning Foundations will provide you. In particular, The Learning Problem will introduce you to the fundamental concepts of machine learning, and Theory of Generalization can help you understand how to generalize test error from training error.
Teacher
Teacher
Teachers are responsible for educating students in a variety of subjects. To teach data science or machine learning, it is helpful to have a strong foundation in the mathematics of machine learning, which Machine Learning Foundations will provide you. In particular, The Learning Problem will introduce you to the fundamental concepts of machine learning, and Theory of Generalization can help you understand how to generalize test error from training error.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. To do so, it is helpful to have a strong foundation in statistics and machine learning, which Machine Learning Foundations can provide you. In particular, The Learning Problem and Types of Learning will introduce you to the fundamental concepts of machine learning and the different types of learning algorithms.
For more career information including salaries, visit:
OpenCourser.com/course/y15l8r/Ji
Reading list
We've selected 18 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations.
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本书是清华大学周志华教授编写的机器学习教材，全面系统地介绍了机器学习的基本原理、算法和应用。内容涵盖了监督学习、无监督学习、强化学习和深度学习，并提供了丰富的习题和编程练习。
本书是机器学习领域的经典教材，全面系统地介绍了机器学习的基本原理和算法。内容涵盖了监督学习、无监督学习、强化学习和贝叶斯学习，并提供了丰富的习题和编程练习。
本书是统计学习领域的重要参考书，全面系统地介绍了统计学习的基本原理和算法。内容涵盖了监督学习、无监督学习和强化学习，并提供了丰富的代码示例和实验结果。
這本書提供了機器學習的數學基礎，涵蓋了從線性代數到機率論的各種主題。
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這本書提供了機器學習的更深入的數學處理，涵蓋了從貝氏定理到支持向量機的各種主題。
這本書提供了機器學習的另一本全面介紹，涵蓋了從決策樹到神經網路的各種主題。
本书是统计学习领域的重要参考书，全面系统地介绍了统计学习的基本原理和算法。内容涵盖了监督学习、无监督学习和强化学习，并提供了丰富的代码示例和实验结果。
Save
本书是机器学习领域的经典教材，全面系统地介绍了机器学习的基本原理和算法。内容涵盖了监督学习、无监督学习、强化学习和深度学习，并提供了丰富的习题和编程练习。
Save
這本書提供了深度學習的全面介紹，涵蓋了從卷積神經網路到遞迴神經網路的各種主題。
本书是机器学习领域的入门教材，通过丰富的案例和代码示例介绍了机器学习的基本原理和算法。内容涵盖了监督学习、无监督学习和强化学习，适合初学者入门学习。
本书是机器学习领域的入门教材，通过丰富的案例和代码示例介绍了机器学习的基本原理和算法。内容涵盖了监督学习、无监督学习和强化学习，适合初学者入门学习。
本书是机器学习领域的入门教材，通过丰富的案例和代码示例介绍了机器学习的基本原理和算法。内容涵盖了监督学习、无监督学习和强化学习，适合初学者入门学习。
本书介绍了机器学习模型的可解释性，全面系统地介绍了可解释性模型的原理、算法和应用。内容涵盖了模型解释、特征重要性和因果推理，并提供了丰富的代码示例和实验结果。
本书是深度学习领域的入门教材，通过Fastai和PyTorch框架介绍了深度学习的基本原理和算法。内容涵盖了卷积神经网络、循环神经网络和生成对抗网络，适合初学者入门学习。
本书是机器学习领域的入门教材，通过Python语言介绍了机器学习的基本原理和算法。内容涵盖了监督学习、无监督学习和强化学习，适合初学者入门学习。
Save
本书是机器学习领域的入门教材，通过算法的角度介绍了机器学习的基本原理和算法。内容涵盖了监督学习、无监督学习和强化学习，适合初学者入门学习。
本书是机器学习领域的入门教材，通过通俗易懂的语言介绍了机器学习的基本原理和算法。内容涵盖了监督学习、无监督学习和强化学习，适合初学者入门学习。
本书是机器学习领域的重要参考书，全面系统地介绍了机器学习在处理大数据方面的原理、算法和应用。内容涵盖了大数据处理、机器学习算法和分布式计算，并提供了丰富的代码示例和实验结果。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y15l8r/Ji
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