Optimization Theory 1: Complete in One Month with 10 Minutes a Day

Name: Optimization Theory 1: Complete in One Month with 10 Minutes a Day
Price: 55000 KRW
Rating: 5 (1 reviews)

This is optimization theory required for AI/deep learning, computer vision, computer graphics, and more. Optimization Theory 1 focuses primarily on the definition of multivariable functions and their derivatives. Why is that? It is because all optimization problems are expressed in the form of multivariable functions. If you acquire an accurate definition of multivariable functions and the concept of differentiation, the theoretical approach to the aforementioned fields will become significantly easier.

(5.0) 1 reviews

80 learners

Level Basic

Course period Unlimited

jhim21

optimization-problem

Linear Algebra

Machine Learning(ML)

Computer Vision(CV)

optimization-problem

Linear Algebra

Machine Learning(ML)

Computer Vision(CV)

What you will gain after the course

Study the definition of multivariable functions required for optimization theory.
Studying the differentiation of multivariable functions
Taylor expansion of multivariable functions
Lecture based on the book: Optimization Theory (written by Jang-hwan Im)

Optimization theory is being used in various fields. To introduce a few representative areas:

AI/Machine Learning and Data Analysis, Computer Vision, Computer Graphics, Applications in Finance, and Natural Sciences etc.

There is an important fact you need to know here. I emphasize it once again!!

"The fact is that all optimization problems are expressed in the form of multivariable functions."

To approach optimization theory, the following two are essential pieces of knowledge.

Accurate understanding of multivariable functions
Accurate differentiation methods for multivariable functions

Many people approach optimization theory without knowing these two facts and end up thinking it is too difficult. This lecture focuses on points 1 and 2. Once you study this, you will eventually encounter these topics again. Finally, I have made an effort to explain everything as simply as possible. However, since this is a field with a certain level of difficulty, it may not be extremely easy. I recommend this lecture to those who study steadily and put in the effort!! Thank you!!

The bible of optimization theory is "S. Boyd, L. Vandenberghe: “Convex Optimization”, Cambridge University Press, 2004.". It is available for download on the internet. If you take a look at this book, you will see that the study of optimization theory is vast and deep. This is also evidence that optimization theory is important and used in many fields. There is a saying, "A journey of a thousand miles begins with a single step." This lecture will be your companion.

"Now! Let's take a look at what we will be studying."

To use optimization theory in that field, mathematical modeling is required.

A mathematical model is expressed as a multivariable function that we call a cost function, and the method of finding the minimizer of this function is called optimization.

Therefore, studying optimization theory mathematically means

Recommended for
these people

Who is this course right for?

Machine learning, deep learning, computer vision, computer graphics, and those in STEM fields
I recommend this to those who want to properly understand the differentiation of multivariable functions.
I recommend this to those who wish to study their major in more depth at graduate school.

Need to know before starting?

Linear Algebra, Calculus
The will to do it is essential.
Those who are willing to invest consistently for one month

Hello
This is jhim21

259

Learners

Reviews

Answers

4.6

Rating

Courses

After graduating with my PhD, I had the opportunity to study and teach computer vision for about five years, which led me to

Up until now, I have been focusing my studies on bridging the gap between my mathematics major and engineering theories.

Areas of Expertise (Fields of Study)

Major: Mathematics (Topological Geometry), Minor: Computer Science

Current) 3D Computer Vision (3D Reconstruction), Kalman Filter, Lie-group (SO(3)),

Researcher in Stochastic Differential Equations

Current) YouTube Channel Host: Jang-hwan Lim: 3D Computer Vision

Current) Facebook Spatial AI KR Group (Mathematics Advisory Committee Member)

Education

PhD in Natural Sciences, University of Kiel, Germany (Major in Topological Geometry & Lie-group, Minor in Computer Science)

Bachelor's and Master's (Topology major) in Mathematics, Chung-Ang University

Experience

Former) CTO of Doobivision, a subsidiary of Daesung Group

Former Research Professor at Chung-Ang University Graduate School of Advanced Imaging (3D Computer Vision Research)

Books:

Optimization Theory: https://product.kyobobook.co.kr/detail/S000200518524

Link

YouTube: https://www.youtube.com/@3dcomputervision

Blog: https://blog.naver.com/jang_hwan_im

er Vision Research) Author of: Optimization Theory: https://product.kyobobook.co.kr/detail/S000200518524 Link YouTube: https://www.youtube.com/@3dcomputervision Blog: https://blog.naver.com/jang_hwan_im

er Vision Research) Author: Optimization Theory: https://product.kyobobook.co.kr/detail/S000200518524 Link YouTube: https://www.youtube.com/@3dcomputervision Blog: https://blog.naver.com/jang_hwan_im

Curriculum

All

17 lectures ∙ (3hr 16min)

Course Materials:

Lecture resources

Section 1. Introduction to Optimization Theory

3 lectures ∙ (42min)

Section 2. Differentiation of multivariable functions

4 lectures ∙ (48min)

4. Understanding Multivariable Functions
17:02
5. partial differentiation
10:48
6. Total differentiation of multivariable functions
13:45
7. Total differentiation of multivariable functions
06:48

Section 3. Taylor expansion of multivariable functions

4 lectures ∙ (33min)

8. Taylor expansion of multivariable functions
12:10
9. Taylor expansion of multivariable functions
13:09
10. Taylor expansion of multivariable functions
02:44
11. Level sets and direction of gradients
05:13

Section 4. Convex Function

2 lectures ∙ (27min)

Section 5. Optimization Theory

3 lectures ∙ (32min)

Section 6. Lagrange multiplier method

1 lectures ∙ (11min)

Published: 01/15/2024

Last updated: 05/13/2025

Reviews

All

1 reviews

5.0

1 reviews

ksg
Reviews 3
∙
Average Rating 5.0
02/19/2025
5
100% enrolled
I'm majoring in Artificial Intelligence, and I'm starting to wonder what kind of AI I was actually learning before. After learning the foundational mathematics, the theory became the basis, and I definitely understand the AI I learned much better and gained a deeper understanding. The content isn't too long either, so it's not boring, and it's a great help to go through it once.

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Optimization Theory 1: Complete in One Month with 10 Minutes a Day

What you will gain after the course

!! All optimization theory problems are expressed as multivariable functions. !!

Key contents covered in this lecture!

Notes before taking the course📢

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Hello
This is jhim21

Curriculum

Reviews

jhim21's other courses

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Optimization Theory 1: Complete in One Month with 10 Minutes a Day

What you will gain after the course

!! All optimization theory problems are expressed as multivariable functions. !!

Key contents covered in this lecture!

Notes before taking the course📢

Recommended for these people

HelloThis is jhim21

Curriculum

Reviews

jhim21's other courses

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Recommended for
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Hello
This is jhim21