課程名稱 |
機器學習中的數學原理 Mathematical Principles of Machine Learning |
開課學期 |
106-2 |
授課對象 |
電機資訊學院 電信工程學研究所 |
授課教師 |
王奕翔 |
課號 |
CommE5051 |
課程識別碼 |
942 U0650 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期三7,8,9(14:20~17:20) |
上課地點 |
明達231 |
備註 |
總人數上限:60人 |
課程網頁 |
http://homepage.ntu.edu.tw/~ihwang/Teaching/Sp18/MPML.html |
課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
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課程概述 |
This course aims to introduce the mathematical principles of machine learning. The course is roughly divided into two parts: the statistical principles, and the optimization principles. For the former, we will focus on introducing basics of statistical learning theory, where the main focus is what can be learned and how good can be learned. For the latter, we will focus on algorithmic aspects of optimization, which play a key role in machine learning. As for theoretical topics, tentatively we aim to cover VC dimension, Rademacher complexity, and oracle complexity. As for learning models, we aim to cover SVM, neural networks, and boosting. |
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 (20%), Homework (40%), Project (40%) |
預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
Lectures will be based on lecture notes and slides. |
參考書目 |
1. Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
2. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, Foundations of Machine Learning, the MIT Press, 2012.
3. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
4. Y. Nesterov, Introductory lectures on convex optimization: A basic course. Kluwer Academic Publishers, 2004.
5. Additional references: research papers and surveys to be assigned during lectures. |
評量方式 (僅供參考) |
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