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

110-1 294843 人工智慧概論 1 3.0 3 ★ 黃育賢
賴建宏 AI雙聯專班一 36 0 110碩一要修，9/25、11/6、11/13、11/20、11/28、12/5

教學大綱與進度：

教師姓名黃育賢

Email yshwang@ntut.edu.tw

最後更新時間 2021-09-20 14:33:06

課程大綱 This course covers concepts, tools, and technologies associated with the design and implementation of data warehousing (DW) and business intelligence (BI) applications. Topics covered include data warehouse architecture and infrastructure, dimensional modeling, Extraction Transformation and Loading (ETL), On Line Analytical Processing (OLAP), data quality, and planning and implementation of a DW & BI application. The course objectives are met through a combination of lectures, class projects and homework assignments. Hands-on experience in developing and deploying a DW & BI application is provided.

課程進度 Week 1. Basic conception A. What's AI, why we need it? B. Branches of AI C. The tribes of machines learning and history D. Machine learning E. Activation Functions F. Use cases a. Spam messages detector b. Web spiders to collect information c. Maze solver Week 2~3. Classification and Regression A. Regression 1. What’s that, and how we can use it? i. Simple regression ii. Multiple regression 2. Linear regularization 3. Nonlinear regularization B. Classification 1. What’s the relationship between regression and classification? i. Binary Classification ii. Multi-Class Classification iii. Multi-Label Classification iv. Imbalanced Classification C. Use cases 1. Boston housing price forecast 2. Breast cancer Wisconsin dataset 3. Decision Tree Classification models to predict employee turnover Week 4. Introduction to Keras Week 5~6. Common neural networks and their applications A. neural networks 1. Let us go “deep” i. Gradient Descent ii. Backpropagation iii. Applied mathematics about AI 2. DNN 3. CNN 4. RNN B. Use cases 1. MNIST 2. Motor fault classification 3. COVID-19 X-Ray Detector

評量方式與標準 Homework (100%) There are 4 assignments based on the cases list in the syllabus. It is subject to change to deal with different goals. It is expected that students can really do each homework every time to enhance their confidence and capability.

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
Artificial Intelligence with Python, 2nd Edition, Packtpub Artificial Intelligence in Finance -- A Python-based Guide, O’Reilly Hands-On Artificial Intelligence for Banking, Packtpub 世界第一簡單機器學習, 世茂出版有限公司世界第一簡單貝氏統計學, 世茂出版有限公司深度學習入門教室：6堂基礎課程+Python實作練習、Deep Learning、人工智慧、機器學習的理論和應用全圖解, 臉譜出版社股份有限公司

課程諮詢管道 Email: yshwang@ntut.edu.tw

課程對應SDGs指標

備註 ●上課方式：分流上課

Microsoft Teams and 綜科211室https://teams.microsoft.com/l/team/19%3a87_8aKKk436O0o01Z7hyhnsZhyi7-yFSM30e-viAiNM1%40thread.tacv2/conversations?groupId=f84a8ab8-5961-42b1-ae44-ff5ccd820648&tenantId=dfb5e216-2b8a-4b32-b1cb-e786a1095218

●評量方式：
In each assignment, two or three people can work together to complete an assignment. In addition to showing the result in the assignment, it is necessary to elaborate the methods used and the difficulties solved.
●補充說明資訊：