Optimization theory 1: 10 minutes a day, 1 month completion

Name: Optimization theory 1: 10 minutes a day, 1 month completion
Price: 55000 KRW
Rating: 5 (1 reviews)

This is the optimization theory required for AI/deep learning, computer vision, computer graphics, etc. Optimization Theory 1 mainly deals with the definition of multivariable functions and differentiation of multivariable functions. Why is that? Because all optimization problems are expressed in the form of multivariable functions. If you acquire the exact definition of multivariable functions and the concept of differentiation, the theoretical approach in the above fields will become considerably easier.

(5.0) 1 reviews

76 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 differentiation of multivariable functions
Taylor expansion of multivariable functions
Book: Lecture based on Optimization Theory (written by Im Jang-hwan)

Optimization theory is being used in a variety of fields. Let me introduce some representative fields.

AI/machine learning and data analysis, computer vision, computer graphics, applications in finance, natural sciences , etc.

Here's an important fact to know. I'll emphasize it again!!

"The fact is that all optimization problems are expressed in multivariable functional form."

If you want to approach optimization theory, the following two things are essential knowledge.

A precise understanding of multivariable functions
Exact differentiation method for multivariable functions

Many people approach optimization theory without knowing these two facts, and end up thinking it's too difficult. This lecture focuses on points 1 and 2. Once you study them, you'll eventually encounter them. Finally, I've tried to explain them simply. However, because this is a somewhat challenging field, it may not be completely easy. I recommend this lecture to anyone who is committed to consistent study and dedication. Thank you!!

The bible of optimization theory is " S. Boyd, L. Vandenberghe: "Convex Optimization", Cambridge University Press, 2004. " It's available for download online. Once you've read this book, you'll realize how vast and profound the study of optimization theory is. This is proof that optimization theory is important and widely used. As the saying goes, "A journey of a thousand miles begins with a single step." This lecture will be your companion.

"Okay! Now, let's look at what we'll be studying."

Mathematical modeling is required to use optimization theory in this field.

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

So, studying optimization theory mathematically means

Recommended for
these people

Who is this course right for?

Machine learning, deep learning, computer vision, computer graphics, and engineering people
I recommend this to those who want to properly understand the differentiation of multivariable functions.
I recommend this to those who want to study their major more deeply in graduate school.

Need to know before starting?

Linear algebra, calculus
The will to do it is essential
Those who will invest consistently for one month

Hello
This is

219

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:

Last updated:

Reviews

All

1 reviews

5.0

1 reviews

ksg
Reviews 2
∙
Average Rating 5.0
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.

$42.90

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