開課班級Class：	授課教師Teacher：	學分數Credits：
碩專資管一	張嘉熒	3

課程大綱Course Description：

本課程涵蓋不同的機器學習方法，主要包括決策術學習、關聯法則學習、分群、支持向量機、貝式網路、增強式學習、基因演算法、基因規劃、類神經網路、蟻群最佳化演算法、粒子群優化、深度學習中之各種方法及其混合應用。其中基因演算法、基因規劃主要包括如何定義不同的基因表示法來代表特定問題的解，再利用遺傳學上有關基因複製、重組、突變等現象及相關技巧來尋找最適當的基因組合以解決該問題。類神經網路部份則將介紹不同類型的網路、它們的適用範圍、及如何訓練類神經網路來解決問題。蟻群最佳化演算法、粒子群優化、深度學習中之各種方法將介紹其方法及廣泛之應用，特別是影像處理、雲端運算與大數據分析方面。

English Outline：

This course mainly includes the introduction of various intelligent computation techniques (Decision Tree Learning, Association Rule Learning, Clustering, Support Vector Machine, Bayesian Networks, Reinforcement Learning, Genetic Algorithms, Genetic Programming, Neural Networks, Ant Colony Optimization, Particle Swarm optimization, various methods in Deep Learning etc) and how to use them to solve problems in different domains. GA and GP parts involve how to define a genetic representation for a problem to be solved, and then apply the evolutionary techniques, such as reproduction, recombination and mutation to produce solutions which suit the problem the most. Different kinds of Neural Networks, such as feed-forward, recurrent, and Hopfield nets, and their corresponding training methods will also be introduced in this course. In addition, Ant Colony Optimization, Particle Swarm optimization, various methods in Deep Learning will be introduced and applied to numerous applications, particularly for image processing, cloud computing and big data.

本科目教學目標Course Objectives：

教學型態Teaching Models：	成績考核方式Grading：
課堂教學+遠距輔助教學(同步、非同步)	平時成績General Performance：50% 期中考Midterm Exam：% 期末考Final exam：% 其它 Other：期末報告50%

參考書目Textbooks/References：

SDGs指標：

UCAN職業項目：

課程更新狀態：

課程匯入時間Import Time：2024-01-23 08:19:00

最後更新時間Last Modified：2024-03-01 16:57:54，更新人modified by：張嘉熒