|
課程名稱 |
機器學習應用概論
Introductory Applied Machine Learning |
|
開課學期 |
112-1 |
|
授課對象 |
生物資源暨農學院 生物產業機電工程學研究所 |
|
授課教師 |
郭彥甫 |
|
課號 |
BME7110 |
|
課程識別碼 |
631 M1580 |
|
班次 |
|
|
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)星期四7(14:20~15:10) |
|
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：15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 |
This course covers the theory and practical algorithms for machine learning from a variety of perspectives. Typical tasks for machine learning are the classification of data, automatic regression, and unsupervised model fitting. Topics covered in this course 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. Students will also work on final projects 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 |
|
|
Office Hours |
|
|
指定閱讀 |
|
|
參考書目 |
1. Tan, Steinbach, and Kumar. 2005. Introduction to Data Mining. Addison Wesley.
2. Bishop. 2007. Pattern Recognition and Machine Learning. Springer.
3. Mitchell. 1997. Machine Learning. McGraw-Hill. |
|
評量方式
(僅供參考) |
|
|