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機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations

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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Syllabus

第九講: Linear Regression

weight vector for linear hypotheses and squared error instantly calculated by analytic solution

第十講: Logistic Regression

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Read about what's good

what should give you pause

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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Reviews summary

機器學習基石下：演算法理論與實踐

根據學生回饋，這門課程是機器學習基礎課程的下半部，主要深入探討機器學習演算法的原理。許多學習者讚揚課程的理論講解深入且清晰非常扎實，能從數學原理上理解演算法的運作。然而，部分學生認為課程難度較高具挑戰性，特別是作業需要花費大量時間，且對數學和程式基礎要求較高。課程更偏重理論而不是實作偏學術風格，對於希望學習大量實作技巧的學生可能需要額外補充。普遍認為這門課是理解機器學習演算法核心概念的絕佳選擇收穫匪淺，但需要做好投入時間和精力克服難度的準備。

課程側重原理，實務coding部分較少。

"課程理論性很強，但實作的部分相對較少，希望能多一些coding練習。"

"如果目標是學習機器學習的實作技巧，這門課可能不是首選，更適合想理解原理的。"

"這門課更像學術入門，實務應用需要自己額外學習或透過作業練習。"

"希望未來課程能稍微平衡一下理論和實作的比例。"

作業挑戰性高，但有助於加深理解。

"作業雖然很難，有時候需要花很多時間，但做完後真的對課程內容理解更深了。"

"這門課的作業設計得很好，能逼著我去思考和實踐理論。"

"作業很有挑戰性，但完成後收穫感很強烈。"

"建議修課前先複習線代和機率，這樣寫作業時會比較順利。"

老師能將複雜概念講解得相對清楚。

"林老師的講解方式非常棒，總能把複雜的概念用簡單的方式說明。"

"即使內容有難度，老師的循序漸進和清晰邏輯讓我能跟得上。"

"每次聽課都覺得老師講解得很到位，能幫助我理解那些抽象的數學公式。"

"我覺得老師的教學非常有條理，能有效傳達知識點。"

課程深入講解機器學習演算法的數學原理。

"這門課把機器學習的演算法原理講得非常透徹，數學推導清晰。"

"我對各種演算法不再只是知道怎麼用，而是理解背後的數學和理論基礎了。"

"課程深度足夠，特別適合想從原理上理解機器學習的人。"

"老師對理論的解釋非常到位，讓我建立了很扎實的基礎知識體系。"

修習前需具備扎實的數學和程式基礎。

"我覺得這門課的難度不低，如果數學（線代、機率）和程式基礎不夠，學起來會比較吃力。"

"建議一定要先修習『機器學習基石上』，並複習好基礎知識。"

"對初學者來說可能太難了，需要花很多額外時間補齊背景知識。"

"如果沒有強的數學背景，可能會跟不上課程進度。"

Activities

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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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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

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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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.