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。]
Offered By
機器學習技法 (Machine Learning Techniques)
National Taiwan UniversityAbout this Course
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Flexible deadlines
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Shareable Certificate
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Intermediate Level
Approx. 18 hours to complete
Chinese (Traditional)
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Intermediate Level
Approx. 18 hours to complete
Chinese (Traditional)
Offered by
Syllabus - What you will learn from this course
2 hours to complete
第一講:Linear Support Vector Machine
2 hours to complete
5 videos (Total 67 min), 4 readings
1 hour to complete
第二講:Dual Support Vector Machine
1 hour to complete
4 videos (Total 60 min)
1 hour to complete
第三講:Kernel Support Vector Machine
1 hour to complete
4 videos (Total 61 min)
1 hour to complete
第四講:Soft-Margin Support Vector Machine
1 hour to complete
4 videos (Total 46 min)
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