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課程名稱 |
最佳化演算法
Optimization Algorithms |
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開課學期 |
112-2 |
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授課對象 |
電機資訊學院 資訊工程學系 |
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授課教師 |
李彥寰 |
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課號 |
CSIE5410 |
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課程識別碼 |
922 U4500 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期二7,8,9(14:20~17:20) |
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上課地點 |
資107 |
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備註 |
總人數上限：30人 |
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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 |
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課程大綱
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課程概述 |
This is a *theory* course. There will not be any programming assignment. The students will have to read and write rigorous mathematical proofs.
This course introduces optimization algorithms for machine learning, in particular first-order convex optimization algorithms, for their scalability with respect to the parameter dimension and sample size. The algorithms this course will cover include gradient descent, mirror descent, proximal gradient methods, the Frank-Wolfe method, and if time allows, stochastic mirror descent. The focus will be non-asymptotic error analysis of these algorithms. |
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課程目標 |
After taking this course, the students are expected to
- be familiar with basic concepts in the black-box approach to convex optimization,
- be able to read literature on optimization theory, and
- be able to choose an appropriate optimization algorithm given a problem. |
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課程要求 |
- The students are expected to be motivated enough to take this course.
- The students are expected to be familiar with multivariate calculus, linear algebra, and probability. Knowledge in machine learning or statistics may be helpful but are not necessary. |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
- Yu. Nesterov. Lectures on Convex Optimization. 2018.
- S. Bubeck. Convex Optimization: Algorithms and Complexity. 2015.
- A. Beck. First-Order Methods in Optimization. 2017.
- G. Lan. First-order and Stochastic Optimization Methods for Machine Learning. 2020.
- Lecture notes by A. Nemirovski: https://www2.isye.gatech.edu/~nemirovs/ |
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評量方式
(僅供參考) |
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