課程資訊
課程名稱
機器學習中的數學原理
Mathematical Principles of Machine Learning 
開課學期
107-2 
授課對象
電機資訊學院  電機工程學研究所  
授課教師
王奕翔 
課號
CommE5051 
課程識別碼
942 U0650 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期二3,4(10:20~12:10)星期四3,4(10:20~12:10) 
上課地點
電二106電二106 
備註
本課程以英語授課。上課時間:週二及週四早上10:30~12:00
總人數上限:60人 
課程網頁
https://cool.ntu.edu.tw/courses/204 
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課程概述

This course aims to introduce some theoretical foundations of machine learning. The course is roughly divided into two parts: (1) the statistical principles and (2) the algorithmic principles. For the former, we will focus on statistical aspects of learning theory, where the main themes are what can be learned and how well a machine can learn from a finite number of training samples. For the latter, we focus on algorithmic aspects of learning theory, where the main theme is how fast a machine can learn with theoretical performance guarantees. 

課程目標
1. Introduce main concepts underlying machine learning with mathematical rigor.
2. Uncover mathematical principles underlying various machine learning techniques.
3. Introduce methods to theoretically analyze learning algorithms.
4. Develop theory-oriented thinking which helps understand existing algorithms and create novel ones. 
課程要求
Prerequisite: Calculus, Probability, Linear Algebra.
Preferable (but optional): Machine learning, Convex optimization, Real analysis.
Grading: Exam (25%), Homework (50%), Project (25%) 
Office Hours
備註: Tuesday and Wednesday, 17:30 - 18:30 
參考書目
1. Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
2. Y. Nesterov, Introductory lectures on convex optimization: A basic course. Kluwer Academic Publishers, 2004.
3. Additional references: research papers and surveys to be assigned during lectures. 
指定閱讀
Lectures will be based on lecture notes and slides. 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Homework 
50% 
 
2. 
Exam 
25% 
 
3. 
Project 
25% 
 
 
課程進度
週次
日期
單元主題