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
1. Learn the basic principles of deep learning
2 How to use deep learning to solve application problems
3 How to use deep learning tools
4 Commonly used deep learning and AI applications
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 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) .
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
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
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 期中考期中考 midterm exam |
30 | 筆試 |
| 期末專案期末專案 Final project |
30 | 分組專案 |
| 作業 作業 Homework |
30 | 回家作業 |
| 出席出席 Attend |
10 | 出席 |
