CSCI3320

Code	Course Offering	CSCI3320
Title	Long Course Title	Fundamentals of Machine Learning	機器學習之基礎課程
Overview	Long Description	The first part introduces basic methods, including minimum error versus maximum likelihood, parametric versus nonparametric estimation, linear regression, factor analysis, Fisher analysis, singular value decomposition, clustering analysis, Gaussian Mixture, EM algorithm, spectral clustering, nonnegative matrix factorization. The second part provides an introduction on small sample size learning, consisting of model selection criteria, RPCL learning, automatic model selection during learning, regularization and sparse learning. Prerequisite: ENGG2040 or ENGG2430 or ESTR2002.	第一部分介紹基本方法，包括最小誤差與最大似然、參數與非參數估計、線性回歸分析、因數分析、費歇判別分析、奇異值分解、聚類分析、高斯混合、EM 演算法、譜聚類、非負矩陣分解。第二部分簡介有限樣本學習，包括模型選擇準則、RPCL 學習、學習過程中自動模型選擇、規則化與稀疏學習。