傳統計算的主要特徵是嚴格、確定和精確，但其並不適合處理現實生活中的許多問題，例如駕駛汽車、下棋、家電控制…等。但軟式計算基於其不確定、不精確及不完全真值的容錯特性，可提供低成本的方案解決許多日常生活中的問題。本課程將介紹軟式計算的基本原理、相關計算模式，及其在人工智慧與機器學習領域的應用。本課程將介紹的軟式計算的計算模式主要包括了: Neural networks、support vector machine、fuzzy logic、evolutionary computation、simulated annealing、swarm intelligence、Bayesian network…等。此外，本課程將搭配相關工具軟體的實際演練，讓學生未來可易於將所學套用在研究與工作上。The main characteristics of traditional calculations are strict, accurate and accurate, but they are not suitable for dealing with many problems in real life, such as driving cars, playing chess, home appliance control... etc. However, based on its inaccurate, inaccurate and incomplete true value, soft calculation can provide low-cost solutions to solve many problems in daily life. This course will introduce the basic principles, related computing modes, and its application in the fields of artificial intelligence and machine learning. The calculation modes of soft computing introduced in this course mainly include: Neural networks, support vector machine, fuzzy logic, evolutionary computing, simulated annealing, swarm intelligence, Bayesian network... etc. In addition, this course will be accompanied by actual practice of related tool software, so that students can easily apply what they have learned to research and work in the future.
1. 蘇木春、張孝德，機器學習：類神經網路、模糊系統以及基因演算法則，第四版，全華圖書股份有限公司，2017 年 3 月
2. 李聯旺、廖珗洲、謝政勳，人工智慧：智慧型系統導論，第三版，全華圖書股份有限公司，2012 年 3 月
3. Sebastian Raschka, Vahid Mirjalili, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, Packt Publishing Limited., September 20, 2017.
4. Prateek Joshi, Artificial Intelligence with Python, Packt Publishing Limited., January 2017.
1. Su Muchun, Zhang Xiaode, Machine Learning: Category Neural Network, Fuzzy Systems and Genetic Algorithm, Fourth Edition, Quanhua Books Co., Ltd., March 2017
2. Li Haowang, Liao Zhou, Xie Zhengxian, Artificial Intelligence: Intelligent Systems Discussion, Third Edition, Quanhua Books Co., Ltd., March 2012
3. Sebastian Raschka, Vahid Mirjalili, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, Packt Publishing Limited., September 20, 2017.
4. Prateek Joshi, Artificial Intelligence with Python, Packt Publishing Limited., January 2017.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 課堂參與課堂參與 Class Participation |
15 | |
| 課堂作業課堂作業 Classroom Works |
35 | |
| 期中考期中考 Midterm exam |
25 | |
| 期末專題期末專題 Final topics |
25 |
