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。]
ofrecido por
機器學習技法 (Machine Learning Techniques)
Universidad Nacional de TaiwánAcerca de este Curso
ofrecido por

Universidad Nacional de Taiwán
We firmly believe that open access to learning is a powerful socioeconomic equalizer. NTU is especially delighted to join other world-class universities on Coursera and to offer quality university courses to the Chinese-speaking population. We hope to transform the rich rewards of learning from a limited commodity to an experience available to all.
Programa - Qué aprenderás en este curso
第一講:Linear Support Vector Machine
more robust linear classification solvable with quadratic programming
第二講:Dual Support Vector Machine
another QP form of SVM with valuable geometric messages and almost no dependence on the dimension of transformation
第三講:Kernel Support Vector Machine
kernel as a shortcut to (transform + inner product): allowing a spectrum of models ranging from simple linear ones to infinite dimensional ones with margin control
第四講:Soft-Margin Support Vector Machine
a new primal formulation that allows some penalized margin violations, which is equivalent to a dual formulation with upper-bounded variables
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