課程名稱 |
機器學習應用概論 Introductory Applied Machine Learning |
開課學期 |
109-1 |
授課對象 |
生物資源暨農學院 生物機電工程學研究所 |
授課教師 |
郭彥甫 |
課號 |
BME7110 |
課程識別碼 |
631 M1580 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期一7,8(14:20~16:20)星期五3(10:20~11:10) |
上課地點 |
生機201生機201 |
備註 |
人工智慧領域核心課程 總人數上限:14人 |
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1091BME7110 |
課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
課程大綱
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課程概述 |
This course covers the theory and practical algorithms for machine learning from a variety of perspectives. Typical tasks are the classification of data, automatic regression and unsupervised model fitting. Topics covered include: statistical learning methods, shrinkage regression, principle component analysis, decision tree learning, support vector machines, artificial neural network, k-means, k-nearest neighbor, and etc. Short theoretical and programming assignments will be given. Student will also work on a final project of their choice. |
課程目標 |
This course is designed to give a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning. |
課程要求 |
Evaluation:
•Homework assignments (biweekly, written and programming): 35%
•Midterm exam: 15% (2 hrs, in-class, closed book)
•Final exam: 15% (2 hrs, closed book or take home)
•Final project: 35% (presentation and report) |
預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
1. Hastie, Tibshirani, and Friedman. 2009. Elements of Statistical Learning, 2nd Ed. Springer. (http://www-stat.stanford.edu/~tibs/ElemStatLearn/download.html)
2. Tan, Steinbach, and Kumar. 2005. Introduction to Data Mining. Addison Wesley. |
參考書目 |
1. Bishop. 2007. Pattern Recognition and Machine Learning. Springer.
2. Mitchell. 1997. Machine Learning. McGraw-Hill. |
評量方式 (僅供參考) |
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週次 |
日期 |
單元主題 |
第1週 |
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Introduction |
第2週 |
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Basic statistics and math review |
第3週 |
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Linear regression |
第4週 |
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PCA, PCR, and PLSR |
第5週 |
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School holiday |
第6週 |
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Project proposal |
第7週 |
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Overfitting |
第8週 |
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Ridge regression and LASSO |
第9週 |
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Midterm exam |
第10週 |
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LDA |
第11週 |
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Project midterm check |
第12週 |
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Support vector machine |
第13週 |
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Decision tree |
第14週 |
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Artificial neural network |
第15週 |
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K‐nearest neighbor, Naive Bayesian, and ensemble methods |
第16週 |
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k‐means and hierarchal clustering |
第17週 |
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Project presentation |
第18週 |
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Final exam |
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