機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
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Course Summary
This course provides a comprehensive overview of mathematical foundations for machine learning. It covers linear algebra, calculus, probability, and statistics, which are essential for understanding and implementing machine learning algorithms.Key Learning Points
- Gain a solid understanding of key mathematical concepts for machine learning
- Learn how to apply mathematical concepts to real-world problems
- Develop the skills necessary to implement and analyze machine learning algorithms
Related Topics for further study
Learning Outcomes
- Understand and apply key mathematical concepts for machine learning
- Implement and analyze machine learning algorithms
- Apply mathematical concepts to real-world machine learning problems
Prerequisites or good to have knowledge before taking this course
- Basic understanding of programming (Python)
- Familiarity with linear algebra and calculus (recommended)
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Quizzes and assignments
Similar Courses
- Mathematics for Machine Learning
- Applied Data Science with Python
Related Education Paths
Related Books
Description
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。]
Outline
- 第一講：The Learning Problem
- Course Introduction
- What is Machine Learning
- Applications of Machine Learning
- Components of Machine Learning
- Machine Learning and Other Fields
- NTU MOOC 課程問題詢問與回報機制
- 課程大綱
- 課程形式及評分標準
- 延伸閱讀
- homework 0
- 第二講：Learning to Answer Yes/No
- Perceptron Hypothesis Set
- Perceptron Learning Algorithm (PLA)
- Guarantee of PLA
- Non-Separable Data
- 第三講：Types of Learning
- Learning with Different Output Space
- Learning with Different Data Label
- Learning with Different Protocol
- Learning with Different Input Space
- 第四講：Feasibility of Learning
- Learning is Impossible?
- Probability to the Rescue
- Connection to Learning
- Connection to Real Learning
- 作業一
- 第五講：Training versus Testing
- Recap and Preview
- Effective Number of Lines
- Effective Number of Hypotheses
- Break Point
- 第六講: Theory of Generalization
- Restriction of Break Point
- Bounding Function: Basic Cases
- Bounding Function: Inductive Cases
- A Pictorial Proof
- 第七講: The VC Dimension
- Definition of VC Dimension
- VC Dimension of Perceptrons
- Physical Intuition of VC Dimension
- Interpreting VC Dimension
- 第八講: Noise and Error
- Noise and Probabilistic Target
- Error Measure
- Algorithmic Error Measure
- Weighted Classification
- 作業二
Summary of User Reviews
Learn the mathematical foundations of machine learning in this comprehensive course on Coursera. Students praise the engaging lectures and practical exercises that help to solidify complex concepts. One key aspect that many users thought was good is the course's emphasis on real-world applications of machine learning.Pros from User Reviews
- Engaging lectures
- Practical exercises
- Emphasis on real-world applications
- Comprehensive coverage of mathematical foundations
- Excellent preparation for further study in machine learning
Cons from User Reviews
- Some users found the pace of the course to be too fast
- Occasional technical issues with exercises
- Not suitable for beginners with no prior experience in machine learning
- Requires a strong background in mathematics
- Some users found the course to be too theoretical
林軒田
4.9 Raiting
機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
- 4.9
-
![]()
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![]()
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Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. [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。]...
機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
- 4.9
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Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. [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。]...
機器學習技法 (Machine Learning Techniques)
- 0.0
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The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features....
