查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

112-1 324826 工業人工智能機器學習 1 3.0 3 ★ 張賢廷機電學院(大) 18 0 張賢廷老師

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
112-1	324826	工業人工智能機器學習	1	3.0	3	★	張賢廷	機電學院(大)	18	0	張賢廷老師

教學大綱與進度：

教師姓名張賢廷

Email htchang@gapp.nthu.edu.tw

最後更新時間 2023-07-04 17:09:22

課程大綱本課程以Python物件導向程式撰寫為出發，輔以要求學生開發各類型之應用來熟悉Python物件導向程式之特點，並且教導學生使用各類型之應用函式庫來讓學生更加熟悉Python之特性，並應用於人工智慧、影像處理與網頁資料抓取…等應用。 Students in this course will begin by writing Python object-oriented programs, followed by developing various types of applications to familiarize themselves with Python object-oriented programs, and are instructed to utilize various application libraries to get acquainted with Python features and to apply them to applications such as artificial intelligence, image processing, web data crawling, etc.

課程進度課程內容將根據實際情況適當調整 The content of the course will be adjusted appropriately according to the actual situation Week 01 Introduction & Instruction of Python tools Week 02 Preliminary Mathematics Review Week 03 Regression Analysis Week 04 MCP Neuron & Adaline Week 05 Bayesian Decision Theory Week 06 Logistic regression Week 07 Support Vector Machine & Kernel trick Week 08 KNN Week 09 Decision Tree Week 10 Dimensionality Reduction Week 11 Performance evaluating Week 12 Hyper-parameter adjusting Week 13 Clustering Analysis Week 14 Pathing Planning Week 15 Convolutional Neural Networks, CNN Week 16 Recurrent Neural Networks, RNN Week 17 Implementation of YOLO & AlphaPose Week 18 Final exam

評量方式與標準課堂表現 10% Project 1 15% Project 2 15% Project 3 15% Project 4 15% Final 30%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
1. Sebastian Raschka, Vahid Mirjalili, “Python Machine Learning: Machine Learning 3/e,” Packt Publishing, 2019 2. Ethem Alpaydin, “Introduction to Machine Learning 4/e,” The MIT Press, 2020 3. Christopher M. Bishop, “Pattern Recognition and Machine Learning,” Springer, 2006

課程諮詢管道有任何問題請利用Email與我聯繫
If there are any questions, please use Email to contact me

Email: htchang@ntut.edu.tw

課程對應SDGs指標

備註

教師姓名	張賢廷
Email	htchang@gapp.nthu.edu.tw
最後更新時間	2023-07-04 17:09:22
課程大綱	本課程以Python物件導向程式撰寫為出發，輔以要求學生開發各類型之應用來熟悉Python物件導向程式之特點，並且教導學生使用各類型之應用函式庫來讓學生更加熟悉Python之特性，並應用於人工智慧、影像處理與網頁資料抓取…等應用。 Students in this course will begin by writing Python object-oriented programs, followed by developing various types of applications to familiarize themselves with Python object-oriented programs, and are instructed to utilize various application libraries to get acquainted with Python features and to apply them to applications such as artificial intelligence, image processing, web data crawling, etc.
課程進度	課程內容將根據實際情況適當調整 The content of the course will be adjusted appropriately according to the actual situation Week 01 Introduction & Instruction of Python tools Week 02 Preliminary Mathematics Review Week 03 Regression Analysis Week 04 MCP Neuron & Adaline Week 05 Bayesian Decision Theory Week 06 Logistic regression Week 07 Support Vector Machine & Kernel trick Week 08 KNN Week 09 Decision Tree Week 10 Dimensionality Reduction Week 11 Performance evaluating Week 12 Hyper-parameter adjusting Week 13 Clustering Analysis Week 14 Pathing Planning Week 15 Convolutional Neural Networks, CNN Week 16 Recurrent Neural Networks, RNN Week 17 Implementation of YOLO & AlphaPose Week 18 Final exam
評量方式與標準	課堂表現 10% Project 1 15% Project 2 15% Project 3 15% Project 4 15% Final 30%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 1. Sebastian Raschka, Vahid Mirjalili, “Python Machine Learning: Machine Learning 3/e,” Packt Publishing, 2019 2. Ethem Alpaydin, “Introduction to Machine Learning 4/e,” The MIT Press, 2020 3. Christopher M. Bishop, “Pattern Recognition and Machine Learning,” Springer, 2006
課程諮詢管道	有任何問題請利用Email與我聯繫 If there are any questions, please use Email to contact me Email: htchang@ntut.edu.tw
課程對應SDGs指標
備註