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Valeriy Kalyagin
and
Sergey Slashchinin
The course is devoted to the systematization of the mathematical background of the students necessary for the successful mastering of educational disciplines in the field of computer vision. The course includes sections of mathematical analysis, probability...
Read more
The course is devoted to the systematization of the mathematical background of the students necessary for the successful mastering of educational disciplines in the field of computer vision. The course includes sections of mathematical analysis, probability...
Read more
The course is devoted to the systematization of the mathematical background of the students necessary for the successful mastering of educational disciplines in the field of computer vision. The course includes sections of mathematical analysis, probability theory, linear algebra.
Aim of the course:
• Systematization of the mathematical background
• Preparation for the use of mathematical knowledge in the professional activities of a specialist in the field of
computer vision.
Practical Learning Outcomes expected:
• Mastering practical skills in mathematics
• The solution of mathematical problems that are encountered in the practical work of a specialist in the field of computer vision.
This Course is part of HSE University Master of Computer Vision degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/r381p.
Register for this course and see more details by visiting:
OpenCourser.com/course/scevnh/mathematics
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Provides a strong foundation in mathematics for computer vision, covering essential topics like linear algebra and probability theory
Taught by recognized experts in the field, Valeriy Kalyagin and Sergey Slashchinin, who bring a wealth of knowledge and experience to the classroom
Part of the HSE University Master of Computer Vision degree program, this course aligns with industry standards and academic rigor
Requires prior mathematical background for successful participation
Focuses on the mathematical fundamentals necessary for computer vision, rather than practical applications
Limited to the mathematical aspects of computer vision, excluding other essential knowledge and skills in the field
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Reviews summary
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There is a single negative review and a single critical review. The reviewers were unhappy with the clarity of the instruction and the final project. Not enough information is available to determine whether this course is generally well received.
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 Mathematics for computer vision with these
activities:
Identify mentors in the field of computer vision
Show steps
Connecting with mentors can help you gain valuable insights, guidance, and support throughout your learning journey.
Browse courses on
Computer Vision
Show steps
-
Attend industry events and conferences to network with professionals.
-
Reach out to professors, researchers, and industry experts via email or LinkedIn.
-
Identify potential mentors who align with your career goals and interests.
Review linear algebra
Show steps
Refreshing knowledge linear algebra will help you perform matrix operations smoothly.
Browse courses on
Linear Algebra
Show steps
-
Revise the basics of linear algebra, such as vectors, matrices, and matrix operations.
-
Practice solving linear equations and systems of equations.
-
Review the concepts of eigenvalues and eigenvectors, and their applications.
Solve practice problems
Show steps
Solving practice problems will help you apply mathematical concepts to real-world scenarios.
Browse courses on
Linear Algebra
Show steps
-
Identify the key concepts and principles involved in the problem.
-
Develop a step-by-step plan to solve the problem.
-
Carry out the plan and check your work.
Two other activities
Expand to see all activities and additional details
Show all five activities
Create a mathematical model
Show steps
Creating a mathematical model will help you understand and apply mathematical concepts to solve complex problems.
Browse courses on
Mathematical Modeling
Show steps
-
Define the problem and identify the relevant variables.
-
Develop a mathematical representation of the problem.
-
Validate the model and make predictions.
-
Interpret the results and draw conclusions.
Participate in computer vision competitions
Show steps
Participating in competitions provides an opportunity to test your skills, gain practical experience, and showcase your portfolio.
Browse courses on
Computer Vision
Show steps
-
Identify relevant competitions that align with your interests and skill level.
-
Form a team or work individually on the competition project.
-
Develop and implement innovative solutions using computer vision techniques.
-
Submit your project and receive feedback on your work.
Identify mentors in the field of computer vision
Show steps
Connecting with mentors can help you gain valuable insights, guidance, and support throughout your learning journey.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Attend industry events and conferences to network with professionals.
- Reach out to professors, researchers, and industry experts via email or LinkedIn.
- Identify potential mentors who align with your career goals and interests.
Review linear algebra
Show steps
Refreshing knowledge linear algebra will help you perform matrix operations smoothly.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revise the basics of linear algebra, such as vectors, matrices, and matrix operations.
- Practice solving linear equations and systems of equations.
- Review the concepts of eigenvalues and eigenvectors, and their applications.
Solve practice problems
Show steps
Solving practice problems will help you apply mathematical concepts to real-world scenarios.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Identify the key concepts and principles involved in the problem.
- Develop a step-by-step plan to solve the problem.
- Carry out the plan and check your work.
Two other activities
Expand to see all activities and additional details
Show all five activities
Create a mathematical model
Show steps
Creating a mathematical model will help you understand and apply mathematical concepts to solve complex problems.
Browse courses on
Mathematical Modeling
Show steps
Show steps
Show steps
- Define the problem and identify the relevant variables.
- Develop a mathematical representation of the problem.
- Validate the model and make predictions.
- Interpret the results and draw conclusions.
Participate in computer vision competitions
Show steps
Participating in competitions provides an opportunity to test your skills, gain practical experience, and showcase your portfolio.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Identify relevant competitions that align with your interests and skill level.
- Form a team or work individually on the competition project.
- Develop and implement innovative solutions using computer vision techniques.
- Submit your project and receive feedback on your work.
Career center
Learners who complete Mathematics for computer vision will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers construct the algorithms that take images from an assortment of sources, process them, and draw conclusions about them. This role draws on a variety of disciplines, including computer science, electrical engineering, and mathematics. Engineers with a solid understanding of mathematics, like the kind developed for this course, will find success in the field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They work closely with Data Scientists to identify the right problems to solve with machine learning, and then they develop and implement the models that will solve those problems. A strong foundation in mathematics is essential for success in this role, as it helps Machine Learning Engineers understand the algorithms they are working with and develop effective models.
Data Scientist
Data Scientist
Data Scientists collect, analyze, and interpret data to help businesses make informed decisions. This can involve developing machine learning models, building data visualization tools, and communicating insights to stakeholders. A strong foundation in mathematics is essential for success in this role, as it helps Data Scientists understand the data they are working with and develop effective models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work on a variety of projects, from small personal apps to large enterprise systems. A strong foundation in mathematics is essential for success in this role, as it helps Software Engineers understand the algorithms and data structures they are working with.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in a variety of settings, including manufacturing, logistics, and healthcare. They work with businesses to improve efficiency, reduce costs, and make better decisions. A strong foundation in mathematics is essential for success in this role, as it helps Operations Research Analysts understand the models they are working with and develop effective solutions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They work in a variety of settings, including investment banks, hedge funds, and asset management companies. A strong foundation in mathematics is essential for success in this role, as it helps Quantitative Analysts understand the models they are working with and make sound investment decisions.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They work in a variety of settings, including insurance companies, consulting firms, and government agencies. A strong foundation in mathematics is essential for success in this role, as it helps Actuaries understand the models they are working with and make sound risk assessments.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. They work in a variety of settings, including businesses, government agencies, and non-profit organizations. A strong foundation in mathematics is essential for success in this role, as it helps Data Analysts understand the data they are working with and draw valid conclusions.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to understand consumer behavior and market trends. They work in a variety of settings, including businesses, consulting firms, and government agencies. A strong foundation in mathematics is essential for success in this role, as it helps Market Researchers understand the data they are working with and draw valid conclusions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of settings, including government agencies, businesses, and research institutions. A strong foundation in mathematics is essential for success in this role, as it helps Statisticians understand the data they are working with and draw valid conclusions.
Financial Analyst
Financial Analyst
Financial Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. They work in a variety of settings, including investment banks, hedge funds, and asset management companies. A strong foundation in mathematics is essential for success in this role, as it helps Financial Analysts understand the models they are working with and make sound investment recommendations.
Business Analyst
Business Analyst
Business Analysts use mathematical and statistical models to analyze business data and make recommendations for improvement. They work in a variety of settings, including consulting firms, corporations, and government agencies. A strong foundation in mathematics is essential for success in this role, as it helps Business Analysts understand the data they are working with and make sound recommendations.
Risk Analyst
Risk Analyst
Risk Analysts use mathematical and statistical models to assess risk and uncertainty. They work in a variety of settings, including financial institutions, insurance companies, and government agencies. A strong foundation in mathematics is essential for success in this role, as it helps Risk Analysts understand the models they are working with and make sound risk assessments.
Teacher
Teacher
Teachers develop and deliver lesson plans, assess student learning, and provide feedback to students. While a strong foundation in mathematics is not always required for this role, it can be helpful for teachers who are teaching mathematics or science.
Writer
Writer
Writers create content for a variety of purposes, including journalism, marketing, and technical writing. While a strong foundation in mathematics is not always required for this role, it can be helpful for writers who are writing about technical topics.
For more career information including salaries, visit:
OpenCourser.com/course/scevnh/mathematics
Reading list
We've selected eight 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
Mathematics for computer vision.
Provides a comprehensive introduction to computer vision, covering topics such as image processing, feature detection, object recognition, and video analysis. It valuable resource for students and researchers in the field of computer vision.
Save
Provides a comprehensive introduction to computer vision, covering topics such as image processing, feature detection, object recognition, and video analysis. It valuable resource for students and researchers in the field of computer vision.
Save
Provides a comprehensive overview of computer vision algorithms and applications, covering topics such as image processing, feature detection, object recognition, and video analysis. It valuable resource for students and researchers in the field of computer vision.
Save
Provides a comprehensive introduction to machine learning for computer vision, covering topics such as image classification, object detection, and semantic segmentation. It valuable resource for students and researchers in the field of computer vision.
Provides a comprehensive introduction to multiple view geometry, which is essential for understanding how 3D scenes can be reconstructed from multiple images. It valuable resource for students and researchers in the field of computer vision.
Provides a comprehensive introduction to deep learning for computer vision, covering topics such as image classification, object detection, and semantic segmentation. It valuable resource for students and researchers in the field of computer vision.
Provides a comprehensive introduction to probabilistic robotics, which fundamental technique for computer vision. It covers topics such as robot localization, mapping, and planning. It valuable resource for students and researchers in the field of computer vision.
Provides a comprehensive introduction to digital image processing, which fundamental technique for computer vision. It covers topics such as image enhancement, image restoration, and image segmentation. It valuable resource for students and researchers in the field of computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/scevnh/mathematics
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Time
52 hours
Via
Coursera
Institution
HSE University
Instructors
Valeriy Kalyagin
Sergey Slashchinin
This course belongs to a
collection
called
Basics in computer vision. It's comprised of three courses:
- 2D image processing
- Object-oriented programming
- Mathematics for computer vision (viewing)
Good to know
Provides a strong foundation in mathematics for computer vision, covering essential topics like linear algebra and probability theory
Taught by recognized experts in the field, Valeriy Kalyagin and Sergey Slashchinin, who bring a wealth of knowledge and experience to the classroom
Part of the HSE University Master of Computer Vision degree program, this course aligns with industry standards and academic rigor
Requires prior mathematical background for successful participation
Focuses on the mathematical fundamentals necessary for computer vision, rather than practical applications
Limited to the mathematical aspects of computer vision, excluding other essential knowledge and skills in the field
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