108年第1學期-5697 深度學習 課程資訊

課程分享

選課分析

本課程名額為 70人,已有17人選讀,尚餘名額53人。

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

授課教師

陳隆彬

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).

課程資訊

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