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。]
Offert par
機器學習技法 (Techniques d'apprentissage automatique)
Université nationale de TaïwanÀ propos de ce cours
Offert par

Université nationale de Taïwan
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.
Programme de cours : ce que vous apprendrez dans ce cours
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
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
À quoi ai-je droit si j'achète le Certificat ?
Une aide financière est-elle possible ?
D'autres questions ? Visitez le Centre d'Aide pour les Étudiants.