課程資訊
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課程基本資訊
| 項目 | 內容 |
| 課程名稱 | 機器學習與深度學習導論 |
| 開課學期 | 110-1 |
| 授課對象 |
土木工程學研究所 土木工程學系 電腦輔助工程組 |
| 授課教師 | 陳俊杉 |
| 課號 | CIE 5133 |
| 課程辨識碼 | 521 U9230 |
| 班次 | |
| 學分 | 3 |
| 全/半年 | 半年 |
| 必/選修 | 選修 |
| 上課時間 | 星期三 2,3,4(9:10~12:10) |
| 上課地點 | 博雅102 |
| 備註 | 第一堂課以抽籤方式決定修課名單。 |
課程大綱
| 項目 | 內容 |
| 課程概述 | This is an introductory course to machine learning and deep learning (short for ML) offered by the Department of Civil Engineering, National Taiwan University. Artificial intelligence, in particular the ML subfield, is now ubiquitous. Using data as its core, the rapid surge and democratization on ML accelerates innovation in many domains. It is thus essential for students with engineering background to acquire basic understanding and ability on the subject. This course is designed to help you to view problems and applications from a ML perspective and to understand principles of ML. There is a fundamental structure to ML thinking and basic principles that should be understood. There are also particular areas where intuition, creativity, common sense, and domain knowledge must be brought to bear. A ML perspective aims to provide students with structure and principles, and this will give you a framework to systematically analyze problems and to develop applications in various domains. |
| 課程目標 | The objective of this course is to provide you with fundamental understanding of ML and with how to apply ML theories and techniques to analyze applications with data. To comprehend the contents, you will need working knowledge on elementary Engineering Mathematics. This course will also emphasize on hand-on experience of doing ML via Python. You need to be familiar with the basic of Python and are willing to learn more. You are advised to take at least one solid introductory course on Python (for example, my freshman Python course) before taking this course. After taking this course, you should be able to: (1) approach problems and applications with ML in mind, (2) understand fundamental principles of ML and (3) use Python to process data and do ML. |
| 課程要求 | 1. working knowledge on elementary Engineering Mathematics 2. familiar with the basic of Python and are willing to learn more. |
| 參考書目 | Principles, Theories and Algorithms 1. James, G., Witten, D., Hastie, T., Tibshirani, R. (2013), An Introduction to Statistical Learning. 2. Tan, P. N., Steinbach M., Karpatne A., Kumar V. (2018), Introduction to Data Mining, Second Edition. 3. Abu-Mostafa, Y S, Magdon-Ismail, M., Lin, H-T (2012) Learning from Data, AMLbook.com. 4. Bishop, C (2006) Pattern Recognition and Machine Learning, Springer. 5. Goodfellow, I., Bengio, Y., Courville, A., Bach, F. (2016) Deep Learning, MIT Press. Python Programming 1. Jake VanderPlas (2016), A Whirlwind Tour of Python, O'Reilly (freely available from http://www.oreilly.com/programming/free/files/a-whirlwind-tour-of-python.pdf) 2. Jake VanderPlas (2016), Python Data Science Handbook, O'Reilly. (online version freely available from https://jakevdp.github.io/PythonDataScienceHandbook) 3. Francois Chollet (2016), Deep Learning with Python, Manning Publications. (Second Edition is underway) |
| 指定閱讀 | lecture notes |
課程進度
| 週次 | 日期 | 單元主題 |
成績評量方式
| 編號 | 項目 | 百分比 | 說明 |
面談時間
| 備註 | 無 |