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機器學習技法 (Machine Learning Techniques)

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. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]

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What's inside

Syllabus

第一講：Linear Support Vector Machine

more robust linear classification solvable with quadratic programming

第二講：Dual Support Vector Machine

another QP form of SVM with valuable geometric messages and almost no dependence on the dimension of transformation

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探討產業標準特徵嵌入、預測性特徵組合、與隱藏式特徵抽取。

由林軒田老師授課，在機器學習領域備受推崇。

課程內容涵蓋線性支持向量機、核支持向量機、軟間距支持向量機等常見演算法。

提供 Blending、Bagging、Adaptive Boosting 等進階技術的介紹。

探討決策樹、隨機森林、梯度提升決策樹等樹狀模型與應用。

涵蓋神經網路、深度學習、徑向基底函數網路等深度學習模型。

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Reviews summary

機器學習進階技術與理論深度解析

學生們表示，這門《機器學習技法》課程提供了機器學習進階技術的扎實理論基礎，對於希望深入理解演算法背後數學原理的學習者來說，是極具價值的。課程內容廣泛而深入，涵蓋了支援向量機 (SVM)、集成學習方法及早期神經網路與深度學習模型等多個重要主題。儘管其理論深度高，對數學背景有一定要求，學生普遍認為林軒田教授的教學清晰且概念解釋透徹。部分評論指出，課程作業具備挑戰性，有助於鞏固學習，但對於部分缺乏實戰經驗或數學基礎不足的學習者而言，可能需要額外投入大量時間。總體而言，這是一門高度推薦的進階課程。

課程作業有助於加深理論理解，但對學生的數學能力有要求。

"作業的設計很巧妙，雖然不涉及大量編碼，但推導和思考的過程讓我對理論理解更深。"

"有幾次作業讓我花費了大量時間，但完成後的那種成就感和對知識的掌握感是無與倫比的。"

"我覺得作業的難度超出了我的預期，如果不是已經有基礎，可能很難獨立完成。"

本課程為進階內容，強烈建議先修讀《機器學習基石》。

"強烈建議先修讀《機器學習基石》，否則這門課會非常難以跟上，數學基礎也需要相當扎實。"

"這課程對於我這種想從基礎進階到高階理論的人來說恰到好處，但對純新手很不友好。"

"沒有扎實的線性代數和微積分基礎，學習起來會非常吃力，需要預先準備。"

林軒田教授的教學條理分明，複雜概念解釋透徹。

"林軒田教授的教學真的是首屈一指，把複雜的概念講得非常清楚，即使是高深的理論也變得易懂。"

"我非常欣賞老師的教學邏輯，一步步引導，讓我在理解機器學習的過程中少走了很多彎路。"

"講義和板書都做得很好，配合教授的講解，學習效率很高。"

深入探討機器學習演算法的數學原理與推導。

"這門課的數學推導非常詳細，對我理解SVM和Boosting的深層機制幫助很大。"

"如果你想理解機器學習的理論基石而不是只會調用庫，這門課絕對是首選。"

"課程內容極具深度，讓我對以前模糊的概念有了清晰的認識。"

深度學習部分較為基礎，可能無法滿足對前沿技術的需求。

"關於深度學習的內容有些基礎，對於目前業界最前沿的應用，還需要額外學習。"

"課程的深度學習部分講的是比較早期和經典的模型，缺乏當下主流的框架與實作介紹。"

"這門課的重點顯然不在最新的深度學習技術上，這點需要注意。"

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 機器學習技法 (Machine Learning Techniques) with these activities:

阅读《机器学习实战》

Show steps

通过阅读经典著作《机器学习实战》，奠定坚实的机器学习基础，了解各种机器学习算法和技术。

View Machine Learning with Python dialysis combat... on Amazon

Show steps

阅读第 1-5 章，了解机器学习的基本概念、数据预处理和模型评估。
阅读第 6-10 章，深入学习支持向量机、决策树和集成学习算法。
尝试书中的练习和项目，以巩固你的理解并应用你的知识。

整理课程笔记和练习题

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通过整理课程笔记和练习题，加深对所学知识的理解，为复习和考试做好准备。

Show steps

回顾讲义，总结关键概念、公式和算法。
整理练习题，分类并按难度排序。
定期复习笔记和练习题，巩固你的理解。

探索支持向量机（SVM）的在线教程

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通过循序渐进的教程，深入了解支持向量机（SVM）的工作原理、不同核函数的影响以及在实际问题中的应用。

Browse courses on SVM

Show steps

完成 Coursera 上关于 SVM 的免费课程或教程。
在 YouTube 和其他在线平台上观看关于 SVM 的视频教程。
尝试使用 Scikit-Learn 等机器学习库实现 SVM 模型。

One other activity

Expand to see all activities and additional details

Show all four activities

SVM 分类练习

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通过解决一系列 SVM 分类练习题，巩固对 SVM 概念的理解，提高解决分类问题的实践能力。

Browse courses on SVM

Show steps

使用 Kaggle 或 LeetCode 等平台上的数据集构建 SVM 分类模型。
尝试不同的内核函数（如线性核、多项式核）并分析其对分类性能的影响。
对模型进行调参，以优化超参数并提高分类精度。

Career center

Learners who complete 機器學習技法 (Machine Learning Techniques) will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers are responsible for building and deploying machine learning models. They work with Data Scientists to identify the right models to use, and then they develop and test the models to ensure they are accurate and efficient. This course will provide a solid foundation in machine learning algorithms and techniques, which are essential for success in this role.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends, relationships, and insights. They use statistical models and machine learning techniques to build dashboards and reports that help businesses make better decisions. This course will provide a strong foundation in machine learning and data analysis techniques, which are essential for success in this role.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. They use statistical models to identify trends and relationships in data. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in statistics.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts are responsible for developing and using mathematical and statistical models to analyze financial data. They use these models to make investment decisions and to manage risk. This course will help build a foundation in machine learning and data analysis techniques, which are essential for success in this role.

See salaries and explore the career path for Quantitative Analyst

Insurance Analyst

Insurance Analysts are responsible for analyzing insurance data to identify trends and develop pricing models. They work with insurance companies to develop and implement strategies to manage risk. This course will help build a foundation in machine learning and data analysis techniques, which are essential for success in this role.

See salaries and explore the career path for Insurance Analyst

Data Scientist

Data Scientists are responsible for collecting, cleaning, and analyzing data. They use statistical models to identify trends, relationships, and insights from data. This course will help build a foundation in machine learning and data analysis techniques, which are essential for success in this role.

See salaries and explore the career path for Data Scientist

Data Engineer

Data Engineers are responsible for designing, building, and maintaining data pipelines. They work with data scientists and analysts to ensure that data is available and reliable. This course may be useful for Data Engineers who want to learn more about machine learning and data analysis techniques, which are increasingly being used in data engineering.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers are responsible for designing, developing, and testing software applications. They work with teams of other engineers to build and maintain software systems. This course may be useful for Software Engineers who want to learn more about machine learning and data analysis techniques, which are increasingly being used in software development.

See salaries and explore the career path for Software Engineer

Risk Analyst

Risk Analysts are responsible for identifying, assessing, and mitigating risks. They work with businesses to develop and implement strategies to manage risk. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in risk management.

See salaries and explore the career path for Risk Analyst

Actuary

Actuaries are responsible for assessing and managing financial risk. They use mathematical and statistical models to develop and implement strategies to manage risk. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in actuarial science.

See salaries and explore the career path for Actuary

Financial Analyst

Financial Analysts are responsible for analyzing financial data to identify trends and develop investment recommendations. They work with businesses and individuals to develop and implement investment strategies. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in financial analysis.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing Analysts are responsible for analyzing marketing data to identify trends and develop marketing strategies. They work with marketing teams to develop and implement strategies to improve marketing effectiveness. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in marketing analysis.

See salaries and explore the career path for Marketing Analyst

Operations Research Analyst

Operations Research Analysts are responsible for developing and using mathematical and statistical models to solve business problems. They work with businesses to identify and solve problems in areas such as logistics, supply chain management, and manufacturing. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in operations research.

See salaries and explore the career path for Operations Research Analyst

Database Administrator

Database Administrators are responsible for designing, building, and maintaining databases. They work with data scientists and analysts to ensure that data is stored and managed efficiently. This course may be useful for Database Administrators who want to learn more about machine learning and data analysis techniques, which are increasingly being used in database management.

See salaries and explore the career path for Database Administrator

Business Analyst

Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. They work with stakeholders to develop and implement solutions that improve efficiency and effectiveness. This course may be useful for Business Analysts who want to learn more about machine learning and data analysis techniques, which are increasingly being used in business analysis.

See salaries and explore the career path for Business Analyst

Reading list

We've selected ten 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 機器學習技法 (Machine Learning Techniques).

Pattern Recognition and Machine Learning

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這是一本經典的教科書，涵蓋了機器學習的基礎概念和演算法。它提供了廣泛的資料和深入的分析，對初學者和進階學習者都很有用。

(English) Pattern Recognition and Machine Learning...

Hardcover

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(English) Pattern Recognition and Machine Learning...

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(English) Pattern Recognition and Machine Learning (text...

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