Sep 8th 2015

機器學習基石 (Machine Learning Foundations) (Coursera)

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Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know.

Welcome! The instructor has decided to teach the course in Mandarin on Coursera, while the slides of the course will be in English to ease the technical illustrations. We hope that this choice can help introduce Machine Learning to more beginners in the Mandarin-speaking world. The English-written slides will not require advanced English ability to understand, though. If you can understand the following descriptions of this course, you can probably follow the slides.

Machine learning is an exciting field with lots of applications in engineering, science, finance, and commerce. It is also a very dynamic field, where many new techniques are being designed every day, and the hot techniques and theories at times can rise and disappear rapidly. Thus, users of machine learning from other fields often face the problem of choosing or using the techniques properly. In this course, we emphasize the necessary fundamentals that give any student of machine learning a solid foundation, and enable him or her to exploit current techniques properly, explore further techniques and theories, or perhaps to contribute their own in the future.

The course roughly corresponds to the first half-semester of the National Taiwan University course "Machine Learning", and the second half-semester is expected to be on Coursera under the name "Machine Learning Techniques" soon in the future. Based on five years of teaching this popular course successfully (including winning the most prestigious teaching award of National Taiwan University) and discussing with many other scholars actively, the instructor chooses to focus on what he believes to be the core topics that every student of the subject should know. The students shall enjoy a story-like flow moving from "When Can Machines Learn" to "Why", "How" and beyond.

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