Online Course
機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
National Taiwan University via Coursera
Overview
Class Central Tips
Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. This second course of the two would focus more on algorithmic tools, and the other course would focus more on mathematical tools. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。我們的兩項姊妹課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。本課程將較為著重方法類的工具，而另一課程將較為著重數學類的工具。]
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
-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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