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In this lecture, we discuss modern convex optimization techniques to solve large-scale machine learning problems that involve big data. Most of machine learning problems are written as convex optimization at their core, and therefore it is important to have an in-depth understanding of convex optimization, to solve large-scale machine learning problems efficiently. Topics will include recent developments in SGD (stochastic gradient descent), proximal gradient descent, Nesterov-type acceleration (FISTA & Smoothing), block coordinate descent, and ADMM (alternating direction method of multipliers).
Time: Wed 13:00am-16:00
Location: Cluster Bd. R509 (학연산클러스터 509호)
References:
- Introductory lectures on convex optimization, Yurii Nesterov, Springer (2004)
- Convex Optimization, Boyd & Vandenberghe
- Numerical Optimization, Nocedal & Wright
Grading: this lecture will follow the format of IC-PBL+ lectures.
TA email: nomar0107@gmail.com
Lecture Notes
- Lecture 01. Introduction [pdf]
- Lecture 02. Background in machine learning [pdf] & optimization [pdf]
- Lecture 03.
- Lecture 04.
- Lecture 05.
- Midterm (Oct 24, in class)
- You can bring one-sided A4 cheating sheet
- Lecture 07.
- AGD [pdf]
- Lecture 08.
- Compressed Sensing [pdf]
- Lecture 09-10.
- ADMM [pdf]
- Lecture 11.
- Lecture 12.
- Duality 3-4 [pdf]
ICCV2019 Paper Review
- Presentation 01. (11/7)
- Presentation 02. (11/14)
- 조형민 SinGAN: Learning a Generative Model from a Single Natural Image [pdf]
- Presentation 03. (11/21)
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