Nov 10th 2015

機器學習技法 (Machine Learning Techniques) (Coursera)

Created by: Delivered by:
Taught by:

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。]

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 students 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. [歡迎大家!這門課將採用英文投影片配合華文的教學講解,我們希望能藉這次華文教學的機會,將機器學習介紹給更多華人世界的同學們。課程中使用的英文投影片不會使用到艱深的英文,如果你能了解以下兩段的課程簡介,你應該也可以了解課程所使用的英文投影片。]

In the prequel of this course, Machine Learning Foundations, we have illustrated the necessary fundamentals that give any student of machine learning a solid foundation to explore further techniques. While many new techniques are being designed every day, some techniques stood the test of time and became popular tools nowadays.

The course roughly corresponds to the second half-semester of the National Taiwan University course "Machine Learning." 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 three of those popular tools, namely embedding numerous features (kernel models, such as support vector machine), combining predictive features (aggregation models, such as adaptive boosting), and distilling hidden features (extraction models, such as deep learning).