返回 登录
0

Coursera公开课:机器学习基础

课程大纲

Each of the following items correspond to approximately one hour of video lecture. [以下的每個小項目對應到約一小時的線上課程]

When Can Machines Learn? [何時可以使用機器學習]
– The Learning Problem [機器學習問題]
– Learning to Answer Yes/No [二元分類]
– Types of Learning [各式機器學習問題]
– Feasibility of Learning [機器學習的可行性]

Why Can Machines Learn? [為什麼機器可以學習]
– Training versus Testing [訓練與測試]
– Theory of Generalization [舉一反三的一般化理論]
– The VC Dimension [VC 維度]
– Noise and Error [雜訊時錯誤]

How Can Machines Learn? [機器可以怎麼樣學習]
– Linear Regression [線性迴歸]
– Linear `Soft’ Classification [軟性的線性分類]
– Linear Classification beyond Yes/No [二元分類以外的分類問題]
– Nonlinear Transformation [非線性轉換]

How Can Machines Learn Better? [機器可以怎麼樣學得更好]
– Hazard of Overfitting [過度訓練的危險]
– Preventing Overfitting I: Regularization [避免過度訓練一:控制調適]
– Preventing Overfitting II: Validation [避免過度訓練二:自我檢測]
– Three Learning Principles [三個機器學習的重要原則]

链接:http://coursegraph.com/coursera_ntumlone

评论