114年第2學期-1596 機器學習 課程資訊

評分方式

評分項目	配分比例	說明
作業/隨堂習作	30
期中考試	30
期末報告	30
出席狀況與平時表現	10

教育目標

這門課程主要介紹「機器學習」所需處理的各類問題，以及所使用的分析方法和模型。課程將以簡單的概念與理論講解各類方法與模型，並以 Python 進行演示。課程結束後，學生們將能夠運用「機器學習」的方法進行分析與建模。

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：四/2,3,4[M442]
修課班級：統計系2-4
修課年級：2年級以上
選課備註：大數據資料群組(110-114適用)

參考書目

1. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.".
2. James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: With applications in python. Springer Nature.