105年第2學期-6193 機器學習 課程資訊

評分方式

評分項目	配分比例	說明
Home Work	20
Project and/or Presentation	20
Midterm	30
Final	30

教育目標

This course will introduce some machine learning algorithms. We will cover most popular algorithms: (a) (Generalized) Linear Model and Regularization; (b) Decision Trees; (c) Support Vector Machines; (d) Neural Networks; (e) Association Rules and Cluster Analysis;(f) Boosting and Random Forests. In order to take this course, it is better you take statistical computing in advance. Or we will focus more on GLM and MA, and skip some algorithms such as SVM and RF.

課程概述

Machine learning is the science of data analysis that automates a massive number of models building. Its process uses data to iteratively detect patterns and adjust models accordingly, and enables computers to learn without explicitly programmed. This course introduces some important concepts and algorithms of machine learning from both theoretical and practical perspective. The topics include, but not limited to: (1) Supervised learning (Linear Models for Regression and Classification, Kernel Smoothing Methods, Decision Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Ensemble learning (Bagging, Boosting, Random Forests). (4) Others (MCMC, Optimization Integration).

基本資料

選修課，學分數：0-3
上課時間：二/9,10,三/B[M442]
修課班級：統計碩博1,統計碩博1
修課年級：年級以上
選課備註：

參考書目

a. The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
b. Machine Learning - A Probabilistic Perspective by Kevin Murphy
c. Pattern Recognition and Machine Learning by Christopher Bishop