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
112-2	330710	機器學習	1	3.0	3	★	蔡偉和	AI雙聯專班	13	0	碩一修,3.2、3.30、4.13 、4.20、5.4、6.1

教學大綱與進度：

教師姓名	蔡偉和　教師諮商時間(Office Hours)
Email	whtsai@ntut.edu.tw
最後更新時間	2024-02-18 12:15:32
課程大綱	This course introduces the concepts, techniques, and applications of machine learning. Topics mainly include Symbolists (e.g., Decision Tree), Connectionists (e.g., Artificial Neural Networks), Bayesians (e.g., Hidden Markov Model), Analogizers (e.g., kNN), and Evolutionaries (e.g., GA), Natural Language Processing and Speech Recognition. The lecture format will be a combination of slide presentation, chalk talk and computer demonstration. In addition to the theoretical study, students are guided to survey the related references, discuss and brainstorm by group, and conduct the practice projects, so that the capability of creativity and innovation can be developed during the course.
課程進度	1. Introduction to Machine Learning (1hr) 2. Supervised Learning, e.g., Decision Tree, NB, kNN, SVM, Hidden Markov Model, and Artificial Neural Networks (9hr) 3. Unsupervised Learning, e.g., K-means Clustering, Hierarchical Clustering (9hr) 4. Machine Learning with WEKA (9hr) 5. Evolutionary Computing (8hr) 6. Natural Language Processing (5hr) 7. Speech Recognition (9hr) 8. Students' Project Presentation (4hr)
評量方式與標準	Attendance and participation 30% Assignments 30% Oral presentation 40%
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. "Data Mining - Practical Machine Learning Tools and Techniques (2nd Edition)" by Ian Witten and Eibe Frank, eBook ISBN: 9780080477022 (available in our school's library) 2. Terje Kristensen, Computational Intelligence, Evolutionary Computing and Evolutionary Clustering Algorithms. Sharjah, U.A.E.: Bentham Science Publishers. 2016 3. Dong Yu and Li Deng, Automatic Speech Recognition, A Deep Learning Approach. Springer, 2015
課程諮詢管道	email: whtsai@ntut.edu.tw
課程對應SDGs指標
備註	若因疫情而暫停實體上課時，將使用MS Teams視訊上課，詳細資訊將公告於北科i學園，或請洽 whtsai@ntut.edu.tw 詢問。