查詢課程之教學大綱、計畫、參考書目、核心能力等資訊

Academic Affairs 教學大綱、計畫及核心能力
瀏覽次數:102
教學大綱暨計畫 Syllabus & Teaching Plan
課程名稱
Course Name
機器學習
Machine Learning
系所課號
Curriculum No.
COE6001
學年期 / 課號
Semester / Serial No
113  - 1  / 7001
修別
Required/Elective
選修
授課方式
Course Type
遠距教學(同步)
開課班級
Class
工程碩
講授-實習-學分
Credits
3-0-3
上課時間教室
Schedule/Classroom
1-EFG/
授課教師(教師所屬系所)
Instructor(Department)
各教師(工程學院)
人數上限
Max
限7人
教師聯絡資訊E-mail及分機(可洽詢教師所屬系所)
Instructor’s E-mail and Ext.(contact the department)
BEX02
備註
Instructor
1130921停開
課程簡介
Course Introduction
Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the data observed. This course introduces the basics of learning theories, the design and analysis of learning algorithms, and some applications of machine learning.

開設學校:台灣大學
開授教師:林軒田
班級人數:500人(保留300人給台大,聯盟學校平均每校約10人)
開課級別:研究所(原則准許大三以上同學修習)
授課語言:英語授課
同步遠距上課時間:星期一 13:20-16:20
◎遠距上課位置:https://www.youtube.com/@hsuantien
◎課程網頁:https://www.csie.ntu.edu.tw/~htlin/course/ml24fall/
教學目標
Teaching Objectives
The course is designed to prepare junior graduate students with a solid background of machine learning and allow them to use machine learning techniques appropriately in their future research or industry projects.
評量方式
Evaluation methods
70% homework
30% project (tentative)
課業輔導時間
Office hours
同步遠距上課時間:星期一 13:20-16:20
教材網站資訊
Teaching Materials
教材不適合上網的理由:智慧財產權
教學計畫附件
Attachment File
※請遵守智慧財產權觀念。不得非法影印教科書。 
週次教學內容與作業進度教學方式備註各週遠距上課網址( 按我進TronClass課程目錄 )
第1週 course introduction; topic 1: when can machines learn? the learning problem 遠距教學 9月2日,homework 0 announced
第2週 learning to answer yes/no; types of learning 遠距教學 9月9日,homework 1 announced
第3週 feasibility of learning; topic 2: why can machines learn? training versus testing 遠距教學 9月16日
第4週 the VC dimension; noise and error 遠距教學 9月23日,homework 2 announced
第5週 topic 3: how can machines learn? linear regression; logistic regression 遠距教學 9月30日
第6週 linear models for classification; nonlinear transformation 遠距教學 10月7日,homework 0 due; homework 1 due; homework 2 due; homework 3 announced
第7週 topic 4: how can machines learn better? hazard of overfitting; regularization 遠距教學 10月14日
第8週 validation; three learning principles 遠距教學 10月21日,homework 3 due; homework 4 announced; final project announced
第9週 topic 5: how can machines learn by embedding numerous features? linear support vector machine; dual support vector machine 遠距教學 10月28日
第10週 kernel support vector machine; soft-margin support vector machine 遠距教學 11月4日,homework 4 due; homework 5 announced
第11週 topic 6: how can machines learn by combining predictive features? blending and bagging; adaptive boosting 遠距教學 11月11日
第12週 decision tree; random forest; gradient boosted decision tree 遠距教學 11月18日,homework 5 due; homework 6 announced
第13週 topic 7: how can machines learn by distilling hidden features? neural network; (preliminary) deep learning 遠距教學 11月25日
第14週 modern deep learning 遠距教學 12月2日,homework 6 due; homework 7 announced
第15週 no class as instructor needs to attend ACML 2024 and NeurIPS 2024; recording: machine learning for modern artificial intelligence 遠距教學 12月9日
第16週 finale 遠距教學 12月16日,homework 7 due
第17週 no class and winter vacation started (really?) 遠距教學 12月23日,final project due
第18週 - 遠距教學