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Yoon Yong Jin
'Learn concept of AI such as machine learning, deep-learning, support vector machine which is related to linear algebra
- Learn how to use linear algebra for AI algorithm.
Read more
'Learn concept of AI such as machine learning, deep-learning, support vector machine which is related to linear algebra
- Learn how to use linear algebra for AI algorithm.
Read more
'Learn concept of AI such as machine learning, deep-learning, support vector machine which is related to linear algebra
- Learn how to use linear algebra for AI algorithm.
- After completing this course, you are able to understand AI algorithm and basics of linear algebra for AI applications.
Register for this course and see more details by visiting:
OpenCourser.com/course/c6e1nr/math
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What's inside
Syllabus
Introduction to AI
Introduction of Linear Algebra
Low operation and linear combination
Read more
Syllabus
Introduction to AI
Introduction of Linear Algebra
Low operation and linear combination
Read more
Linearly independent and Inverse Matrix
Determinant of Square Matrix and Eigenvalue Problem
Diagonaliztion Problem and AI Applications
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches linear algebra and AI algorithms, which are essential for understanding AI
Suitable for learners with no prior knowledge of linear algebra or AI
Incorporates a mix of theoretical concepts and practical applications
Covers core concepts of linear algebra for AI applications
Save this course
Save Math for AI beginner part 1 Linear Algebra to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Math for AI beginner part 1 Linear Algebra with these
activities:
Review course materials
Show steps
Taking time to review course materials, both before and during the course, can significantly improve retention of learned material.
Show steps
-
Review the syllabus and make note of important topics.
-
Read the assigned readings before each class.
-
Take notes during class and review them after class.
Review linear algebra basics
Show steps
Concepts like matrix operations and the determinant are needed to understand many AI algorithms. Brushing up on these concepts beforehand will ensure you're ready for the course.
Browse courses on
Linear Algebra
Show steps
-
Go over your notes from a previous linear algebra course.
-
Review online resources like Khan Academy or MIT OpenCourseWare.
-
Work through practice problems to test your understanding.
Create a mind map of AI concepts
Show steps
Creating a mind map is a great way to visualize the relationships between different AI concepts. This will help you to better understand the overall landscape of AI.
Browse courses on
AI Concepts
Show steps
-
Start by writing down the main concept in the center of a piece of paper.
-
Draw branches off of the main concept and write down related concepts.
-
Continue to add branches and concepts until you have a comprehensive mind map.
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on AI algorithms
Show steps
There are many great tutorials available online that can help you learn about AI algorithms. Following these tutorials will give you a deeper understanding of the concepts covered in the course.
Browse courses on
AI Algorithms
Show steps
-
Find tutorials on reputable websites like Coursera, edX, or Udemy.
-
Choose tutorials that are appropriate for your level of experience.
-
Follow the tutorials step-by-step and complete the exercises.
Solve practice problems on AI algorithms
Show steps
Solving practice problems is a great way to test your understanding of AI algorithms and to improve your problem-solving skills. Many resources are available online for your practice.
Browse courses on
AI Algorithms
Show steps
-
Find practice problems on websites like LeetCode or HackerRank.
-
Choose problems that are appropriate for your level of experience.
-
Solve the problems and check your solutions against the provided answer key.
Develop a small AI project
Show steps
Developing a small AI project is the best way to apply the concepts you've learned in the course and to gain practical experience. Design a project that interests you and challenge yourself to complete it.
Show steps
-
Choose a project idea that is within your skill level.
-
Gather the necessary resources, such as data, libraries, and tools.
-
Develop and implement your project.
-
Test and evaluate your project.
-
Document your project and share it with others.
Review course materials
Show steps
Taking time to review course materials, both before and during the course, can significantly improve retention of learned material.
Show steps
Show steps
Show steps
- Review the syllabus and make note of important topics.
- Read the assigned readings before each class.
- Take notes during class and review them after class.
Review linear algebra basics
Show steps
Concepts like matrix operations and the determinant are needed to understand many AI algorithms. Brushing up on these concepts beforehand will ensure you're ready for the course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Go over your notes from a previous linear algebra course.
- Review online resources like Khan Academy or MIT OpenCourseWare.
- Work through practice problems to test your understanding.
Create a mind map of AI concepts
Show steps
Creating a mind map is a great way to visualize the relationships between different AI concepts. This will help you to better understand the overall landscape of AI.
Browse courses on
AI Concepts
Show steps
Show steps
Show steps
- Start by writing down the main concept in the center of a piece of paper.
- Draw branches off of the main concept and write down related concepts.
- Continue to add branches and concepts until you have a comprehensive mind map.
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on AI algorithms
Show steps
There are many great tutorials available online that can help you learn about AI algorithms. Following these tutorials will give you a deeper understanding of the concepts covered in the course.
Browse courses on
AI Algorithms
Show steps
Show steps
Show steps
- Find tutorials on reputable websites like Coursera, edX, or Udemy.
- Choose tutorials that are appropriate for your level of experience.
- Follow the tutorials step-by-step and complete the exercises.
Solve practice problems on AI algorithms
Show steps
Solving practice problems is a great way to test your understanding of AI algorithms and to improve your problem-solving skills. Many resources are available online for your practice.
Browse courses on
AI Algorithms
Show steps
Show steps
Show steps
- Find practice problems on websites like LeetCode or HackerRank.
- Choose problems that are appropriate for your level of experience.
- Solve the problems and check your solutions against the provided answer key.
Develop a small AI project
Show steps
Developing a small AI project is the best way to apply the concepts you've learned in the course and to gain practical experience. Design a project that interests you and challenge yourself to complete it.
Show steps
Show steps
Show steps
- Choose a project idea that is within your skill level.
- Gather the necessary resources, such as data, libraries, and tools.
- Develop and implement your project.
- Test and evaluate your project.
- Document your project and share it with others.
Career center
Learners who complete Math for AI beginner part 1 Linear Algebra will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course in Math for AI beginners, part 1 Linear Algebra can help build a foundation for a career as a Machine Learning Engineer. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics are essential for understanding AI algorithms and their applications in machine learning.
Data Scientist
Data Scientist
Data Scientists use AI techniques such as machine learning, deep learning, and support vector machines to extract insights from data. This course in Math for AI beginners, part 1 Linear Algebra can help build a foundation for a career as a Data Scientist. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics are essential for understanding AI algorithms and their applications in data science.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze and model financial data. This course in Math for AI beginners, part 1 Linear Algebra can help build a foundation for a career as a Quantitative Analyst. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics are essential for understanding the mathematical and statistical techniques used in quantitative analysis.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course in Math for AI beginners, part 1 Linear Algebra can help build a foundation for a career as an Actuary. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics are essential for understanding the mathematical and statistical techniques used in actuarial science.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency and effectiveness of organizations. This course in Math for AI beginners, part 1 Linear Algebra can help build a foundation for a career as an Operations Research Analyst. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics are essential for understanding the mathematical and analytical techniques used in operations research.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help organizations make informed decisions. This course in Math for AI beginners, part 1 Linear Algebra can help build a foundation for a career as a Statistician. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics are essential for understanding the mathematical and statistical techniques used in statistics.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and maintain computer systems and software. This course in Math for AI beginners, part 1 Linear Algebra may be useful for Computer Scientists who want to work on AI projects. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics can help Computer Scientists understand the mathematical foundations of AI and how to apply AI techniques in their work.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This course in Math for AI beginners, part 1 Linear Algebra may be useful for Software Developers who want to work on AI projects. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics can help Software Developers understand the mathematical foundations of AI and how to apply AI techniques in their work.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and market trends. This course in Math for AI beginners, part 1 Linear Algebra may be useful for Market Researchers who want to work on AI projects. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics can help Market Researchers understand the mathematical foundations of AI and how to apply AI techniques in their work.
Systems Analyst
Systems Analyst
Systems Analysts analyze and design computer systems to meet the needs of organizations. This course in Math for AI beginners, part 1 Linear Algebra may be useful for Systems Analysts who want to work on AI projects. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics can help Systems Analysts understand the mathematical foundations of AI and how to apply AI techniques in their work.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by analyzing their business processes and recommending changes. This course in Math for AI beginners, part 1 Linear Algebra may be useful for Business Analysts who want to work on AI projects. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics can help Business Analysts understand the mathematical foundations of AI and how to apply AI techniques in their work.
Data Architect
Data Architect
Data Architects design and manage data systems to meet the needs of organizations. This course in Math for AI beginners, part 1 Linear Algebra may be useful for Data Architects who want to work on AI projects. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics can help Data Architects understand the mathematical foundations of AI and how to apply AI techniques in their work.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course in Math for AI beginners, part 1 Linear Algebra may be useful for Software Engineers who want to work on AI projects. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics can help Software Engineers understand the mathematical foundations of AI and how to apply AI techniques in their work.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help organizations make informed decisions. This course in Math for AI beginners, part 1 Linear Algebra may be useful for Data Analysts who want to work on AI projects. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics can help Data Analysts understand the mathematical foundations of AI and how to apply AI techniques in their work.
Financial Analyst
Financial Analyst
Financial Analysts use mathematical and analytical techniques to evaluate investments and make recommendations to clients. This course in Math for AI beginners, part 1 Linear Algebra may be useful for Financial Analysts who want to work on AI projects. The course covers topics such as low operation and linear combination, linearly independent and inverse matrix, determinant of square matrix and eigenvalue problem, diagonalization problem, and AI applications. These topics can help Financial Analysts understand the mathematical foundations of AI and how to apply AI techniques in their work.
For more career information including salaries, visit:
OpenCourser.com/course/c6e1nr/math
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Math for AI beginner part 1 Linear Algebra.
This classic textbook provides a comprehensive introduction to linear algebra, covering the topics of vector spaces, matrices, determinants, and eigenvalues. It is commonly used as a textbook in undergraduate linear algebra courses and is written in a clear and accessible style that makes it suitable for both students and professionals.
This textbook provides a comprehensive introduction to the mathematical foundations of machine learning, including linear algebra, probability, and optimization. It is written in a clear and accessible style and is suitable for both students and professionals.
This textbook provides a practical introduction to linear algebra, with a focus on applications in machine learning, signal processing, and other fields. It covers the topics of vectors, matrices, linear systems, and eigenvalues, and includes numerous examples and exercises.
This textbook provides a practical introduction to machine learning using Python, covering the topics of data preprocessing, feature engineering, model selection, and model evaluation. It is written in a clear and accessible style and is suitable for both students and professionals.
This textbook provides a practical introduction to deep learning using Python, covering the topics of neural networks, convolutional neural networks, and recurrent neural networks. It is written in a clear and accessible style and is suitable for both students and professionals.
This textbook provides a practical introduction to deep learning using PyTorch, covering the topics of neural networks, convolutional neural networks, and recurrent neural networks. It is written in a clear and accessible style and is suitable for both students and professionals.
Save
This textbook provides a comprehensive introduction to AI, covering the topics of machine learning, natural language processing, and computer vision. It is written in a clear and accessible style and is suitable for both students and professionals.
This textbook provides a practical introduction to machine learning, covering the topics of supervised learning, unsupervised learning, and deep learning. It is written in a clear and accessible style and is suitable for both students and professionals.
This textbook provides a comprehensive treatment of matrix analysis and applied linear algebra, covering the topics of matrix theory, determinants, eigenvalues, and singular value decomposition. It valuable reference for students, researchers, and professionals in various fields.
Save
This textbook provides a comprehensive introduction to deep learning, including the topics of neural networks, convolutional neural networks, and recurrent neural networks. It is written in a clear and accessible style and is suitable for both students and professionals.
This textbook provides a rigorous and abstract introduction to linear algebra, covering the topics of vector spaces, linear transformations, and inner product spaces. It is written in a clear and concise style and is suitable for students with a strong mathematical background.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c6e1nr/math
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Level
Introductory
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Institution
Korea Advanced Institute of Science and Technology(KAIST)
Instructor
Yoon Yong Jin
Language
English
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Good to know
Teaches linear algebra and AI algorithms, which are essential for understanding AI
Suitable for learners with no prior knowledge of linear algebra or AI
Incorporates a mix of theoretical concepts and practical applications
Covers core concepts of linear algebra for AI applications
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