We may earn an affiliate commission when you visit our partners.
Working with Matrices
Andy Brown,
Andrew Paster,
Anthony Navarro,
Tarin Ziyaee,
Elecia White,
Cezanne Camacho,
and
Sebastian Thrun
Andy Brown,
Andrew Paster,
Anthony Navarro,
Tarin Ziyaee,
Elecia White,
Cezanne Camacho,
and
Sebastian Thrun
This course will focus on two tools which are vital to self-driving car engineers: object oriented programming and linear algebra.
Register for this course and see more details by visiting:
OpenCourser.com/course/qsjhhc/working
What's inside
Syllabus
An introduction to the amazing tools and algorithms you'll learn in this lesson.
Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm and implement a one-dimensional tracker of your own.
Read more
Syllabus
An introduction to the amazing tools and algorithms you'll learn in this lesson.
Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm and implement a one-dimensional tracker of your own.
Read more
In this lesson, students will learn about representing the state of a car in programming as classes and objects and mathematically as vectors that can be changed with linear algebra!
Linear Algebra is a rich branch of math and a useful tool. In this lesson you'll learn about the matrix operations that underly multidimensional Kalman Filters.
Practice using your object oriented programming and matrix math skills by filling out the methods in a partially-completed `Matrix` class.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores tools and algorithms that are fundamental to self-driving car engineering
Provides foundational knowledge in object oriented programming and linear algebra
Emphasizes mathematical tools that enhance understanding of vehicle tracking algorithms
Students will have the opportunity to apply their OOP and linear algebra skills through practical exercises
Save this course
Save Working with Matrices to your list so you can find it easily later:
Save
Save
Activities
Coming soon
We're preparing activities for
Working with Matrices.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Working with Matrices will develop knowledge and skills
that may be useful to these careers:
Mathematician
Mathematician
Mathematicians explore abstract mathematical concepts and develop new theories. The topics covered in "Working with Matrices", including linear algebra and matrix operations, form the basis of many mathematical research areas and provide a strong foundation for pursuing a career in mathematics.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models used in various applications such as natural language processing, image recognition, and predictive analytics. A solid understanding of linear algebra, covered in "Working with Matrices", is critical for working with multidimensional data and developing efficient algorithms for machine learning models.
Data Scientist
Data Scientist
Data Scientists use scientific methods and statistical techniques to extract knowledge from data, enabling businesses to make informed decisions. The linear algebra concepts covered in "Working with Matrices" are essential for understanding and manipulating high-dimensional datasets, as well as developing predictive models and analyzing complex data relationships.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to inform decision-making. The linear algebra concepts taught in "Working with Matrices" are essential for understanding statistical models, analyzing complex datasets, and developing statistical methods.
Actuary
Actuary
Actuaries assess and manage financial risks for insurance companies and other financial institutions. The knowledge of linear algebra gained in "Working with Matrices" is highly valuable for understanding and modeling complex financial scenarios, pricing insurance policies, and evaluating risk.
Data Analyst
Data Analyst
Data Analysts leverage their knowledge of mathematics, statistics, and computer programming to translate raw data into actionable insights for businesses. With the foundational knowledge gained in "Working with Matrices", learners can build a strong foundation for the linear algebra skills needed to analyze large datasets. This course introduces matrix operations and vector representations, essential concepts for manipulating and interpreting data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Linear algebra, a core topic in "Working with Matrices", provides the foundation for understanding financial risk, portfolio optimization, and developing trading strategies. This course enhances the skills needed to succeed in this quantitative field.
Software Engineer
Software Engineer
Software Engineers apply engineering principles to design, develop, and maintain software systems. Object-oriented programming, a key component of "Working with Matrices", is a fundamental concept for organizing and structuring code, making it essential for Software Engineers to master. Understanding linear algebra provides a strong foundation for working with complex data structures and algorithms.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to optimize operations and decision-making processes in various industries. The linear algebra concepts in "Working with Matrices" provide a strong foundation for modeling and solving complex operational problems, such as supply chain management, scheduling, and resource allocation.
Computer Scientist
Computer Scientist
Computer Scientists research, design, and develop computer systems and applications. The object-oriented programming concepts in "Working with Matrices" provide a strong foundation for understanding software design and development, essential skills for Computer Scientists.
Financial Analyst
Financial Analyst
Financial Analysts use financial data and analysis to make investment recommendations and advise clients on financial matters. Understanding linear algebra, as taught in "Working with Matrices", is beneficial for analyzing financial statements, modeling financial scenarios, and developing risk management strategies.
Business Analyst
Business Analyst
Business Analysts bridge the gap between business and technology, analyzing business needs and designing solutions. The object-oriented programming concepts in "Working with Matrices" provide a foundation for understanding software development and data analysis, essential skills for Business Analysts.
Electrical Engineer
Electrical Engineer
Electrical Engineers design, develop, and maintain electrical systems and devices. While "Working with Matrices" may not directly relate to the core electrical engineering field, the linear algebra concepts covered in this course can be applied to areas such as signal processing, control systems, and power systems analysis.
Mechanical Engineer
Mechanical Engineer
Mechanical Engineers design, develop, and maintain mechanical systems and devices. The concepts taught in "Working with Matrices", such as vector representations and linear transformations, can be applied to areas of mechanical engineering such as structural analysis, robotics, and fluid mechanics.
Civil Engineer
Civil Engineer
Civil Engineers design, build, and maintain infrastructure projects such as roads, bridges, and buildings. While "Working with Matrices" may not directly relate to the core civil engineering field, the linear algebra concepts covered in this course can provide a foundation for understanding structural mechanics and analysis.
For more career information including salaries, visit:
OpenCourser.com/course/qsjhhc/working
Reading list
We've selected 13 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
Working with Matrices.
Provides a comprehensive introduction to linear algebra, covering the core concepts and algorithms that are essential for understanding and applying linear algebra in various fields. It valuable reference tool for students and professionals who want to develop a solid foundation in linear algebra.
Offers a rigorous and comprehensive treatment of linear algebra, making it suitable for advanced undergraduate and graduate students. It provides a deeper understanding of the subject and valuable resource for those who want to pursue advanced studies in mathematics or related fields.
Provides a balanced and accessible introduction to linear algebra, with a focus on applications in various fields such as computer science, engineering, and economics. It commonly used textbook in undergraduate linear algebra courses.
Save
Covers advanced topics in matrix computations, including numerical methods for solving linear systems, eigenvalue problems, and matrix decompositions. It valuable reference for researchers and professionals who need to perform complex matrix computations.
Provides a comprehensive introduction to numerical linear algebra, with a focus on algorithms and their implementation. It covers topics such as direct and iterative methods for solving linear systems, eigenvalue problems, and matrix decompositions.
Provides a comprehensive introduction to machine learning, covering both theoretical foundations and practical algorithms. It valuable resource for students and professionals who want to understand the principles and applications of machine learning.
Save
Provides a comprehensive introduction to deep learning, covering the fundamental concepts, architectures, and algorithms. It valuable resource for students and researchers who want to understand the principles and applications of deep learning.
Save
Provides a comprehensive introduction to computer vision, covering the fundamental algorithms and techniques used in the field. It valuable resource for students and professionals who want to understand the principles and applications of computer vision.
Save
Provides a comprehensive introduction to computer graphics, covering the fundamental principles, algorithms, and techniques used in the field. It valuable resource for students and professionals who want to understand the principles and applications of computer graphics.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It covers the fundamental concepts and algorithms used in machine learning, with a focus on probabilistic modeling and inference. It valuable resource for students and researchers who want to understand the principles and applications of machine learning.
Provides a comprehensive introduction to pattern recognition and machine learning, covering the fundamental concepts and algorithms used in the field. It valuable resource for students and professionals who want to understand the principles and applications of pattern recognition and machine learning.
Provides a comprehensive introduction to algorithmic learning theory, covering the fundamental concepts and algorithms used in the field. It valuable resource for students and researchers who want to understand the principles and applications of algorithmic learning theory.
Provides a comprehensive introduction to convex optimization, covering the fundamental concepts and algorithms used in the field. It valuable resource for students and professionals who want to understand the principles and applications of convex optimization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qsjhhc/working
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Time
50400 hours
Level
Intermediate
Via
Udacity
Instructors
Andy Brown
Andrew Paster
Anthony Navarro
Tarin Ziyaee
Elecia White
Cezanne Camacho
Sebastian Thrun
Language
English
Good to know
Explores tools and algorithms that are fundamental to self-driving car engineering
Provides foundational knowledge in object oriented programming and linear algebra
Emphasizes mathematical tools that enhance understanding of vehicle tracking algorithms
Students will have the opportunity to apply their OOP and linear algebra skills through practical exercises
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser