課程資訊
 課程名稱 最佳化演算法Optimization Algorithms 開課學期 109-1 授課對象 電機資訊學院  資訊工程學研究所 授課教師 李彥寰 課號 CSIE5410 課程識別碼 922 U4500 班次 學分 3.0 全/半年 半年 必/選修 選修 上課時間 星期二7,8,9(14:20~17:20) 上課地點 新504 備註 總人數上限：40人 課程簡介影片 核心能力關聯 核心能力與課程規劃關聯圖 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 Caveat: This is a theory course. ALL of the homework and exam problems ask you to work out mathematical proofs. This is a course on optimization for machine learning. Therefore, we will focus on the black-box approach and first-order methods. We will NOT study classical topics such as linear programming and semi-definite programming. This course consists of two parts. The first part introduces basic notions in convex analysis and standard first-order convex optimization algorithms. The second part focuses on solving online optimization and minimax problems. Below is a tentative list of topics. - Basic convex analysis I: Convexity, strong convexity, Lipschitzness, smoothness, optimality condition, etc. - Gradient descent. - Mirror descent. - Acceleration. - Proximal methods. - Smoothing. - Frank-Wolfe method. - Basic convex analysis II: Subgradient & subdifferential. - Mirror descent revisited. - Online mirror descent. - Learning in games. - Follow the regularized leader. - Optimistic methods. 課程目標 After taking this course, the students are expected to have the ability to: ．Characterize the pros and cons of an optimization algorithm. ．Choose an appropriate optimization algorithm for a given application scenario. ．Do basic complexity analyses of optimization algorithms. ．Read advanced literature in optimization and algorithm design. 課程要求 Prerequisites: Multivariate calculus, linear algebra, and probability. Knowledge in convex analysis, statistics, and/or machine learning are helpful but not necessary. 預期每週課後學習時數 Office Hours 每週五 16:30~18:00每週二 17:20~21:00 備註： 1. Yen-Huan's office hour: After class every Tuesday or by appointment. 2. TA's office hour: 16h30--18h00 every Friday @ Lab 407, CSIE-Der Tian Hall. 指定閱讀 待補 參考書目 1. Yu. Nesterov. 2004. Introductory Lectures on Convex Optimization. 2. S. Bubeck. 2015. Convex Optimization: Algorithms and Complexity. (Available online: http://sbubeck.com/Bubeck15.pdf) 3. A. Ben-Tal and A. Nemirovski. 2015. Lectures on Modern Convex Optimization.(Available online: http://www2.isye.gatech.edu/~nemirovs/LMCO_LN.pdf) 4. N. Cesa-Bianchi and G. Lugosi. 2006. Prediction, Learning, and Games. 5. S. Bubeck. 2011. Introduction to Online Optimization. (Available online: http://sbubeck.com/BubeckLectureNotes.pdf) 6. S. Shalev-Shwartz. 2012. Online Learning and Online Convex Optimization. 7. E. Hazan. 2016. Introduction to Online Convex Optimization. (Available online: http://ocobook.cs.princeton.edu/) 評量方式(僅供參考)
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