第一週 Introduction to Data Science and Matrix Algebra 第二週 Data Collection: Survey sampling 第三週 Data Collection: Factorial design and Space-filling design 第四週 Data Analysis: Supervised Learning I – Linear model and Generalized linear model 第五週 Data Analysis: Supervised Learning II -- Nonparametric regression 第六週 Data Analysis: Supervised Learning III -- Gaussian process regression 第七週 Data Analysis: Supervised Learning IV -- Discriminate analysis 第八週 Data Analysis: Supervised Learning V -- Support vector machine 第九週 期中考週 第十週 Data Analysis: Supervised Learning VI -- Bagging, Random forests, Boosting 第十一週 Data Analysis: Supervised Learning VII -- Deep neural networks 第十二週 Data Analysis: Unsupervised Learning I – Principal component analysis, Factor analysis, Canonical correlation analysis 第十三週 Data Analysis: Unsupervised Learning II -- Clustering methods 第十四週 Big Data Issue I (p>>n): Feature screening 第十五週 Big Data Issue II (n>>p): Subdata selection 第十六週 期末考週 第十七週 彈性教學 第十八週 彈性教學

The aim of this course is to introduce a variety of Statistical and Machine Learning data analysis methods. Three core techniques for data science: Data collection, Supervised learning, and Unsupervised learning, are introduced in detail. Some recent developments in Big/High-dimensional data are involved. The software I will be using for the course is R (website: https://www.r-project.org/).

Basic statistical concepts/theories and programming techniques are required, where the course Data5004 Statistical Foundations of Data Science (I) is helpful for understanding the materials in this course. This course will be graded by Homework assignments (20%), Project presentations (40%), and Paper presentations (40%). Students can use any software, not limited to R, for programming in their project presentations. 建議先至教師個人網頁查看去年的課程內容以決定是否適合：https://sites.google.com/view/mcchang/teaching?authuser=0

教科書: 1. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021), An Introduction to Statistical Learning: with Applications in R, 2nd Edition, Springer 參考書目: 1. Johnson, R.A. and Wichern, D.W. (2007), Applied Multivariate Statistical Analysis,6th edition, Prentice Hall. 2. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2009), The Elements of Statistical Learning, 2nd Edition, Springer 3. Fan, J., Li, R., Zhang, C.-H., and Zou, H. (2020), Statistical Foundations of Data Science, CRC Press 4. Selective Papers