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機器學習技法 (Machine Learning Techniques)

National Taiwan University via Coursera

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Overview

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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).


Syllabus

Each of the following items correspond to approximately one hour of video lecture. [以下的每個小項目對應到約一小時的線上課程]

Embedding Numerous Features [嵌入大量的特徵]
-- Linear Support Vector Machine [線性支持向量機]
-- Dual Support Vector Machine [對偶支持向量機]
-- Kernel Support Vector Machine [核型支持向量機]
-- Soft-Margin Support Vector Machine [軟式支持向量機]
-- Kernel Logistic Regression [核型羅吉斯迴歸]
-- Support Vector Regression
[支持向量迴歸]


Combining Predictive Features [融合預測性的特徵]
-- Bootstrap Aggregation [自助聚合法]
-- Adaptive Boosting [漸次提昇法]
-- Decision Tree [決策樹]
-- Random Forest [隨機森林]
-- Gradient Boosted Decision Tree [梯度提昇決策樹]

Distilling Hidden Features [萃取隱藏的特徵]
-- Neural Network [類神經網路]
-- Deep Learning [深度學習]
-- Radial Basis Function Network
[逕向基函數網路]
-- Matrix Factorization [矩陣分解]

Summary [總結]

Taught by

Hsuan-Tien Lin

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