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
110-2
301363
機器學習
1
3.0
3
★
曾恕銘
蔡偉和
AI雙聯專班一
34
0
110碩一要修,3/5、3/12、3/26、4/10、4/24、5/21
教學大綱與進度:
教師姓名
蔡偉和
Email
whtsai@ntut.edu.tw
最後更新時間
2022-01-21 16:55:14
課程大綱
The goal of this course is to introduce ideas and principles behind machine learning. We will teach the learning strategies including supervised, unsupervised, and reinforcement. The structures of machine learning including Symbolists (Decision Tree etc.), Bayesian Statistics, Analogizers (kNN, K-means etc.), Evolutionaries (GA etc.), Deep learning, Natural Language Processing and Speech Recognition will be elaborated. The course also provides case studies including 5G wireless resource allocation, video scene boundary detection and speech chatbots.
課程進度
1. Introduction to Machine Learning (1.5hr) 2. Supervised Learning, e.g., Decision Tree, kNN, SVM (6hr) 3. Unsupervised Learning, e.g., K-means Clustering, Hierarchical Clustering (3hr) 4. Machine Learning with WEKA (6hr) 5. Bayesian Statistics (4.5) 6. Evolutionary Computing (3hr) 7. Case Study 1: Radio Resource Allocation (6hr) 8. Case Study 2: Video Coding and Scene Boundary Detection (6hr) 9. Case Study 3: Natural Language Processing (9hr) 10. Case Study 4: Speech Recognition (9hr)
評量方式與標準
Attendance and participation 20% Lab report 80%
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. Tan, Steinbach, Karpatne, Kumar, Introduction to Data Mining, 2e, 2019 2. Mendenhall and Sincich, Statistics for engineering and the sciences, 5/e, Prentice Hall. 3. Terje Kristensen, Computational Intelligence, Evolutionary Computing and Evolutionary Clustering Algorithms. Sharjah, U.A.E.: Bentham Science Publishers. 2016 4. Dong Yu and Li Deng, Automatic Speech Recognition, A Deep Learning Approach. Springer, 2015
課程諮詢管道
備註
若因疫情而暫停實體上課時,將使用Google Meet 視訊上課,會議網址將公告於北科i學園,或請洽 whtsai@ntut.edu.tw 詢問。