機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
National Taiwan University via Coursera
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Overview
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Syllabus
- 第九講: Linear Regression
- weight vector for linear hypotheses and squared error instantly calculated by analytic solution
- 第十講: Logistic Regression
- gradient descent on cross-entropy error
to get good logistic hypothesis - 第十一講: Linear Models for Classification
- binary classification via (logistic) regression; multiclass classification via OVA/OVO decomposition
- 第十二講: Nonlinear Transformation
- nonlinear model via nonlinear feature transform+linear model with price of model complexity
- 第十三講: Hazard of Overfitting
- overfitting happens with excessive power, stochastic/deterministic noise and limited data
- 第十四講: Regularization
- minimize augmented error, where the added regularizer effectively limits model complexity
- 第十五講: Validation
- (crossly) reserve validation data to simulate testing procedure for model selection
- 第十六講: Three Learning Principles
- be aware of model complexity, data goodness and your professionalism
Taught by
Hsuan-Tien Lin, 林軒田
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