Online Course
機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
National Taiwan University via Coursera
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135
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Overview
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Syllabus
-what machine learning is and its connection to applications and other fields
第二講:Learning to Answer Yes/No
-your first learning algorithm (and the world's first!) that "draws the line" between yes and no by adaptively searching for a good line based on data
第三講:Types of Learning
-learning comes with many possibilities in different applications, with our focus being binary classification or regression from a batch of supervised data with concrete features
第四講:Feasibility of Learning
-learning can be "probably approximately correct" when given enough statistical data and finite number of hypotheses
第五講:Training versus Testing
-what we pay in choosing hypotheses during training: the growth function for representing effective number of choices
第六講: Theory of Generalization
-test error can approximate training error if there is enough data and growth function does not grow too fast
第七講: The VC Dimension
-learning happens if there is finite model complexity (called VC dimension), enough data, and low training error
第八講: Noise and Error
-learning can still happen within a noisy environment and different error measures
Taught by
Hsuan-Tien Lin
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