課程名稱 |
機器學習應用概論 Introductory Applied Machine Learning |
開課學期 |
110-1 |
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 |
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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 (written and programming): 60%
Midterm exam: 10% (2 hrs, in-class, open or closed book)
Final exam: 10% (2 hrs, in-class, open or closed book)
Final project: 20% (presentation and report)
o Level of challenge: 10%
o Midterm presentation: 3%
o Final presentation + report: 7%
No makeup exams shall be made except for those who have valid reasons of absences, and can present official documents that prove the reasons of absences |
預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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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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