Notes of Machine Learning Techniques

This is an course at coursera instructed by Hsuan-Tien Lin(林轩田) from National Taiwan University.

Machine Learning Techniques

The course extends the fundamental tools in Machine Learning Foundations to powerful and practical models by 3 directions, which includes embedding numerous features, combining predictive features, and distilling hidden features.

About the Course

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 the 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.

Intro

In the prequel of this course, Machining 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 5 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 3 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).