107年第1學期-5697 深度學習 課程資訊

評分方式

評分項目	配分比例	說明
期中考	30	筆試
期末專案	30	分組專案
作業	30	回家作業
出席	10	出席

教育目標

1 學習深度學習基礎原理 2 如何使用深度學習來解決應用問題 3 如何使用深度學習工具 4 常用的深度學習與AI應用

課程概述

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).

基本資料

選修課，學分數：3-0
上課時間：二/2,3,4[ST436]
修課班級：資工碩1,2
修課年級：年級以上
選課備註：三大領域：人工智慧

參考書目

1 Deep Reinforcement Learning, David Silver, Google DeepMind, 2017 (http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-iclr2015.pdf)
2 Reinforcement Learning: An Introduction, by Richard S. Sutton,‎ Andrew G. Barto, A Bradford Book, 2017
3 Other Internet Resources