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Mathematics

Linear Algebra Special Lecture for Machine Learning, AI, Data Scientists, and Engineering Majors

This is an in-depth linear algebra course for engineering majors in fields such as Machine Learning, AI, Robotics, and Computer Vision. It is recommended for those who wish to study linear algebra in depth. While explaining as simply as possible, it covers advanced topics through selective focus and concentration.

3 learners are taking this course

Level Basic

Course period Unlimited

Python
Python
Machine Learning(ML)
Machine Learning(ML)
Deep Learning(DL)
Deep Learning(DL)
Procession
Procession
Python
Python
Machine Learning(ML)
Machine Learning(ML)
Deep Learning(DL)
Deep Learning(DL)
Procession
Procession

What you will gain after the course

  • Linear Algebra for Engineering Majors such as Machine Learning, AI, Robotics, and Computer Vision

  • In detail, from the simplest possible explanations to in-depth theories.

  • Use specific examples and provide very detailed explanations for parts requiring mathematical theory.

Multidimensional Data Representation: Various types of data, such as images, text, and voice, are represented as multidimensional vectors. Linear algebra is essential for handling and transforming such multidimensional data.

Image Processing: In the field of computer vision, images are represented as matrices. Matrix operations are used to transform and filter images, which is primarily utilized in Convolutional Neural Networks (CNNs).

Linear Transformation: Machine learning models, such as neural networks, primarily use linear transformations (matrix multiplication) to transform and process input data. Linear algebra provides the tools to handle these transformations effectively.

Matrix Decomposition: Linear algebraic techniques such as Singular Value Decomposition (SVD) or Eigenvalue Decomposition are used in dimensionality reduction techniques like Principal Component Analysis (PCA) and help in extracting important features.

Optimization Problems: Optimization problems frequently used in machine learning are based on linear algebraic concepts. For example, parameter updates to minimize a loss function are related to matrix calculus and gradient descent.


Studying the topics mentioned above requires a significant amount of time and effort. If you want to demonstrate something immediately, programming implementation also takes a lot of time. If you end up studying in graduate school, there are many cases where mathematics becomes a major obstacle.

Having majored in mathematics and spent many years researching and studying alongside engineering students at an engineering college, I have put a lot of thought into how to study mathematics in a way that is easier yet still true to the nature of the subject.

Therefore, in this lecture, I have chosen the method of solving specific examples first and then using them to study the theory.

Also, if there is too much content, people often get exhausted before even starting. I have designed the curriculum so that the content is easy to follow, yet packed with all the essential information you need to study effectively.

I have made an effort to ensure that even those who are busy can complete the course by consistently investing 15 to 30 minutes a day.

Recommended for
these people

Who is this course right for?

  • Engineering majors in fields such as Machine Learning, AI, Robotics, and Computer Vision

  • Highly recommended for those who want to study linear algebra deeply and properly.

Need to know before starting?

  • The will to do it is essential

  • Those who will study consistently without giving up until they understand.

Hello
This is jhim21

247

Learners

12

Reviews

8

Answers

4.7

Rating

6

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 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

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 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 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

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Curriculum

All

35 lectures ∙ (8hr 33min)

Course Materials:

Lecture resources
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