|
課程名稱 |
資料科學與機器學習入門
Introduction to Data Science and Machine Learning |
|
開課學期 |
112-1 |
|
授課對象 |
理學院 地理環境資源學研究所 |
|
授課教師 |
亞歷山卓 |
|
課號 |
Geog7126 |
|
課程識別碼 |
228EM9690 |
|
班次 |
|
|
學分 |
3.0 |
|
全/半年 |
半年 |
|
必/選修 |
選修 |
|
上課時間 |
星期二7,8,9(14:20~17:20) |
|
上課地點 |
地理二教室 |
|
備註 |
本課程以英語授課。
總人數上限：25人
外系人數限制：3人 |
|
|
|
|
課程簡介影片 |
|
|
核心能力關聯 |
本課程尚未建立核心能力關連 |
|
課程大綱
|
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
|
課程概述 |
The course provides an introduction to the field of data science and machine learning, inserted in the background of geospatial information technologies and big data analytics. The course first introduces the domain and the emergence of data science as the convergence of several trends (internet of things, digitalization, open data, social media, etc.). It then assesses the technical and methodological ecosystem associated to data science, involving data mining tools and machine learning algorithms. The education modules cover the whole workflow of data science, from problem definition, to data preparation, feature extraction, model training, model evaluation and implementation. Besides providing a solid theorical foundation, examples of practical real-world applications will be used to illustrate the various phases of data science projects. |
|
課程目標 |
The course will review the main principles, methods and tools that are used by data scientists and analysts in research and industry.
Through geospatial-related examples and use cases, students are expected to learn how to initiate a data science project, address data analytics challenges, and effectively apply machine learning methodologies. |
|
課程要求 |
The course requires some basic math and statistics knowledge. Whereas a coding background is not strictly needed, knowledge of programming languages is an advantage.
The grading is split into a written examination to assess the acquired theoretical knowledge, and a practical examination to assess the acquired applied capabilities. |
|
預期每週課後學習時數 |
|
|
Office Hours |
另約時間 備註： Monday afternoon and Thursday afternoon (only upon agreement by email) |
|
指定閱讀 |
|
|
參考書目 |
• An Introduction to Statistical Learning (G. James, D. Witten, T. Hastie, R. Tibshiran) – publisher: Springer.
• Data Mining: Practical Machine Learning Tools and Techniques (I. Witten, E. Frank, M. Hall, C. Pal) – publisher: Morgan Kaufmann.
• Introduction to Machine Learning with Python: A Guide for Data Scientists (A. Müller, S. Guido) – publisher: O'Reilly Media.
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (A. Géron) – publisher: O'Reilly Media. |
|
評量方式
(僅供參考) |
|
|
週次 |
日期 |
單元主題 |
|
第1週 |
9/05 |
General course introduction |
|
第2週 |
9/12 |
Definitions of data science and machine learning |
|
第3週 |
9/19 |
Data analysis (1): cleaning and exploration |
|
第4週 |
9/26 |
Data analysis (2): preparation and evaluation (regression task) |
|
第5週 |
10/03 |
Practical session (regression task) |
|
第6週 |
10/10 |
NO CLASS (National Holiday) |
|
第7週 |
10/17 |
Data analysis (3): preparation and evaluation (classification task) |
|
第8週 |
10/24 |
Practical session (classification task) |
|
第9週 |
10/31 |
Dimensionality reduction |
|
第10週 |
11/07 |
Clustering |
|
第11週 |
11/14 |
ML models (1): linear regression / logistic regression |
|
第12週 |
11/21 |
ML models (2): support vector machines |
|
第13週 |
11/28 |
ML models (3): decision trees |
|
第14週 |
12/05 |
ML models (4): ensemble learning |
|
第15週 |
12/12 |
ML models (5): artificial neural networks (to be confirmed) |
|
第16週 |
12/19 |
FINAL EXAM |
|