机器学习支持向量机基础台湾大学课件.pdf

Machine Learning Techniques (機器學習技法) Lecture 1: Linear Support Vector Machine Hsuan-Tien Lin (林軒田) htlin@csie.ntu.edu.tw Department of Computer Science Information Engineering National Taiwan University (國立台灣大學資訊工程系) Hsuan-Tien Lin (NTU CSIE) Machine Learning Techniques 0/28 Linear Support Vector Machine Course Introduction Course History NTU Version ? 15-17 weeks (2+ hours) ? highly-praised with English and blackboard teaching Coursera Version ? 8 weeks of ‘foundations’ (previous course) + 8 weeks of ‘techniques’ (this course) ? Mandarin teaching to reach more audience in need ? slides teaching improved with Coursera’s quiz and homework mechanisms goal: try making Coursera version even better than NTU version Hsuan-Tien Lin (NTU CSIE) Machine Learning Techniques 1/28 Linear Support Vector Machine Course Introduction Course Design from Foundations to Techniques ? mixture of philosophical illustrations, key theory, core algorithms, usage in practice, and hopefully jokes :-) ? three major techniques surrounding feature transforms: ? Embedding Numerous Features: how to exploit and regularize numerous features? —inspires Support Vector Machine (SVM) model ? Combining Predictive Features: how to construct and blend predictive features? —inspires Adaptive Boosting (AdaBoost) model ? Distilling Implicit Features: how to identify and learn implicit features? —inspires Deep Learning model allows students to use ML professionally Hsuan-Tien Lin (NTU CSIE) Machine Learning Techniques 2/28 Linear Support Vector Machine Course Introduction Fun Time Which of the following description of this course is true? 1 the course will be taught in Taiwanese 2 the course will tell me the techniques that create the android Lieutenant Commander Data in Star Trek 3 the course will be 16 weeks long 4 the course will focus on three major techniques Reference Answer: 4 1 no, my Taiwanese is unfortunately not good enough for teaching (yet) 2 no, although what we teach may serve as building blocks