課程大綱： 機器學習基礎 – 算法基礎培訓
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
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