為使修習者熟悉數據分析方法了解基礎統計推論之理論及技術再加上進階統計應用方法如迴歸分析與變異數分析之訓練使具備統計分析之概念與技術

To make practitioners familiar with data analysis methods, understand the theories and techniques of basic statistical recommendations, plus advanced statistical application methods such as reproductive analysis and variable number analysis training to provide the concepts and techniques of statistical analysis

Machine learning is the science of data analysis that enables computers to learn without being explicitly programmed. From the computer science point of view, unlike computational statistics dealing with prediction-making or data mining focusing on data-exploring, machine learning uses data to iteratively detect patterns and adjust models accordingly. This introductory course provides students an overview of the field of machine learning, as well as of its fundamental concepts and algorithms from practical perspective. Usually, machine learning algorithms are categorized as being supervised or unsupervised. Some of the important topics include (1) Supervised learning (Linear and Logistic Regressions, Classification and Regression Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Others (Boosting and Random Forests).

Machine learning is the science of data analysis that enables computers to learn without being explicitly programmed. From the computer science point of view, unlike computing statistics dealing with prediction-making or data mining focusing on data-exploring, machine learning uses data to iteratively detect patterns and adjust models accordingly. This introduction course provides students an overview of the field of machine learning, as well as of its fundamental concepts and algorithms from practical perspective. Usually, machine learning algorithms are classified as being supervised or unsupervised. Some of the important topics include (1) Supervised learning (Linear and Logistic Regressions, Classification and Regression Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Others (Boosting and Random Forests).

陳可杰,黃聯海,李宗倚,李婉怡,陳益昌譯 Anderson, Sweeney, and Williams,2013, Statistics for Business and Economics, 11th Ed.,滄海

Chen Kejie, Huang Lianhai, Li Zongyi, Li Wanyi, Chen Yichangru Anderson, Sweeney, and Williams, 2013, Statistics for Business and Economics, 11th Ed., Hua Hai