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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 second course of the two would focus more on algorithmic tools, and the other course would focus more on mathematical 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。本課程將較為著重方法類的工具，而另一課程將較為著重數學類的工具。]
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What's inside
Syllabus
第九講: Linear Regression
weight vector for linear hypotheses and squared error instantly calculated by analytic solution
第十講: Logistic Regression
Read more
Syllabus
第九講: Linear Regression
weight vector for linear hypotheses and squared error instantly calculated by analytic solution
第十講: Logistic Regression
Read more
gradient descent on cross-entropy error
to get good logistic hypothesis
第十一講: Linear Models for Classification
binary classification via (logistic) regression; multiclass classification via OVA/OVO decomposition
第十二講: Nonlinear Transformation
nonlinear model via nonlinear feature transform+linear model with price of model complexity
第十三講: Hazard of Overfitting
overfitting happens with excessive power, stochastic/deterministic noise and limited data
第十四講: Regularization
minimize augmented error, where the added regularizer effectively limits model complexity
第十五講: Validation
(crossly) reserve validation data to simulate testing procedure for model selection
第十六講: Three Learning Principles
be aware of model complexity, data goodness and your professionalism
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
介紹機器學習的基礎演算法工具。
課程講師林軒田為機器學習領域的資深研究者。
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Save 機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations to your list so you can find it easily later:
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Reviews summary
Ml foundations: algorithmic foundations
This course is a theoretical dive into the foundations of machine learning. It covers topics such as linear regression, logistic regression, and regularization. Students find the material challenging but rewarding, and they appreciate the instructor's expertise. However, some students have noted that the assignments can sometimes be unclear and that the course could benefit from more practical examples.
Students appreciate the instructor's expertise.
"林老师讲课很好，很认真准备的课程！"
Students find the course challenging but rewarding.
"The course is moderately difficult and challenging"
"This course covers ML theory, which involves many math derivations, and is thus much difficult than Andrew Ng's ML course."
The course could benefit from more practical examples.
"内容很有趣，但是如果能有更多关于code实战的材料或内容会更好"
Some students have noted that the assignments can sometimes be unclear.
"還不錯的課程但有時作業的題意不至清楚"
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 機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations with these
activities:
閱讀《深度學習》
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這本書提供了深度學習領域的全面概述，補充了課程中介紹的演算法和技術。
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深度學習
on Amazon
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-
閱讀特定章節，以補充課程材料
-
摘要關鍵概念並撰寫筆記
-
與其他學生討論書中的內容
Show all one activities
閱讀《深度學習》
Show steps
這本書提供了深度學習領域的全面概述，補充了課程中介紹的演算法和技術。
View
深度學習
on Amazon
Show steps
Show steps
Show steps
- 閱讀特定章節，以補充課程材料
- 摘要關鍵概念並撰寫筆記
- 與其他學生討論書中的內容
Show all one activities
Career center
Learners who complete 機器學習基石下 (Machine Learning Foundations)---Algorithmic 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 designing, building, and deploying machine learning models. They work closely with data scientists to understand the business problem and translate it into a technical solution. This course provides a strong foundation in the algorithms and techniques of machine learning, which is essential for machine learning engineers.
Academic
Academic
Academics are responsible for teaching and conducting research in a variety of fields, including machine learning. This course provides a foundation in the algorithms and techniques of machine learning, which can help academics develop new and innovative machine learning methods and teach their students about the latest advances in the field.
Data Scientist
Data Scientist
Data scientists are responsible for collecting, analyzing, and interpreting data to extract meaningful insights. Machine learning is a key tool for data scientists, as it allows them to automate the process of learning from data and making predictions. This course teaches the foundational algorithms and techniques of machine learning, which can help data scientists build more effective and accurate models.
Research Scientist
Research Scientist
Research scientists are responsible for conducting research in a variety of fields, including machine learning. This course provides a foundation in the algorithms and techniques of machine learning, which can help research scientists develop new and innovative machine learning methods.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. Machine learning is increasingly being used by statisticians to automate many of the tasks that they perform, and it can also help statisticians to identify more complex patterns in data. This course provides a foundation in the algorithms and techniques of machine learning, which can help statisticians work more efficiently and effectively.
Data Analyst
Data Analyst
Data analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. Machine learning can be used to automate many of the tasks that data analysts perform, and it can also help data analysts to identify more complex patterns in data. This course provides a foundation in the algorithms and techniques of machine learning, which can help data analysts work more efficiently and effectively.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts are responsible for using mathematical and statistical models to analyze financial data and make investment decisions. Machine learning is increasingly being used by quantitative analysts to develop more sophisticated and accurate models. This course provides a foundation in the algorithms and techniques of machine learning, which can help quantitative analysts improve the performance of their models.
Actuary
Actuary
Actuaries are responsible for using mathematical and statistical models to assess and manage risk. Machine learning is increasingly being used by actuaries to develop more sophisticated and accurate risk models. This course provides a foundation in the algorithms and techniques of machine learning, which can help actuaries improve the performance of their risk models.
Software Engineer
Software Engineer
Software engineers are responsible for developing and maintaining software applications. Machine learning is increasingly being used to enhance software applications, and software engineers who are familiar with machine learning can develop more innovative and effective applications. This course provides a foundation in the algorithms and techniques of machine learning, which can help software engineers build better machine learning-powered applications.
Investor
Investor
Investors are responsible for investing money in a variety of assets, including stocks, bonds, and real estate. Machine learning is increasingly being used by investors to develop more sophisticated and accurate investment models. This course provides a foundation in the algorithms and techniques of machine learning, which can help investors improve the performance of their investment portfolios.
Business Analyst
Business Analyst
Business analysts are responsible for understanding the business needs of an organization and translating them into technical requirements. Machine learning can be used to improve the efficiency and accuracy of business analysis, and business analysts who are familiar with machine learning can develop more effective solutions to business problems. This course provides a foundation in the algorithms and techniques of machine learning, which can help business analysts use machine learning to improve their work.
Operations Research Analyst
Operations Research Analyst
Operations research analysts are responsible for using mathematical and statistical models to solve operational problems. Machine learning can be used to improve the efficiency and accuracy of operations research models, and operations research analysts who are familiar with machine learning can develop more effective solutions to operational problems. This course provides a foundation in the algorithms and techniques of machine learning, which can help operations research analysts use machine learning to improve their work.
Product Manager
Product Manager
Product managers are responsible for managing the development and marketing of products. Machine learning is increasingly being used to develop new and innovative products, and product managers who are familiar with machine learning can develop more successful products. This course provides a foundation in the algorithms and techniques of machine learning, which can help product managers use machine learning to improve their products.
Consultant
Consultant
Consultants are responsible for providing advice and support to organizations on a variety of topics, including machine learning. This course provides a foundation in the algorithms and techniques of machine learning, which can help consultants develop more effective solutions to business problems involving machine learning.
Entrepreneur
Entrepreneur
Entrepreneurs are responsible for starting and running their own businesses. Machine learning can be used to develop new and innovative products and services, and entrepreneurs who are familiar with machine learning can develop more successful businesses. This course provides a foundation in the algorithms and techniques of machine learning, which can help entrepreneurs use machine learning to improve their businesses.
For more career information including salaries, visit:
OpenCourser.com/course/87oksk/Ji
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
機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations.
這本書是一個全面的統計學習教科書，提供了機器學習基本概念、演算法和理論的深入介紹。它涵蓋了本課程中的關鍵主題，例如線性回歸、邏輯回歸、分類模型和過度擬合。
這本書是機器學習領域的經典著作，提供了深入的理論和演算法基礎。它涵蓋了本課程中討論的許多概念，並提供了更深入的技術細節。
這本書通過使用 Python 庫提供了一個實作導向的機器學習介紹。它提供了一個擴展本課程中討論的概念的實際工具包。
Save
這本書提供了一個機器學習的全面視角，涵蓋廣泛的主題。它提供了本課程中介紹的概念的高級理解。
這本書提供了使用 R 語言進行機器學習的實作導向，非常適合希望使用 R 來實作機器學習解決方案的讀者。
這本書提供了一個機率觀點來探討機器學習，這是理解機器學習許多基本概念的關鍵。它提供了本課程中討論的主題的替代觀點。
儘管這本書更偏重於人工智能的一般概念，但它提供了機器學習基礎的全面概述，對於理解機器學習在更廣泛的背景中的作用非常有用。
Save
儘管這本書超出了本課程的範圍，但它提供了深度學習領域的重要介紹，這對於理解機器學習的未來方向非常有用。
這本書提供了機器學習的一個有趣且互動式的介紹，非常適合那些希望以一種有趣且容易理解的方式學習基本概念的讀者。
這本書提供了一個機器學習的入門介紹，非常適合對該主題不熟悉或需要複習基本概念的讀者。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/87oksk/Ji
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