Mathematics for Machine Learning: Multivariate Calculus
Master essential multivariate calculus concepts for machine learning, from gradients to optimization techniques.
Master essential multivariate calculus concepts for machine learning, from gradients to optimization techniques.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Mathematics for Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
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What you'll learn
Understand and apply multivariate calculus concepts to machine learning
Implement gradient descent and optimization techniques
Master neural network mathematics and backpropagation
Apply Taylor series for function approximation
Perform linear and non-linear regression analysis
Skills you'll gain
This course includes:
3.3 Hours PreRecorded video
25 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course provides a solid foundation in multivariate calculus essential for machine learning. Starting with basic concepts like gradients, the course progresses through advanced topics including neural networks, optimization, and Taylor series. Students learn to apply mathematical tools to real machine learning problems, with a focus on practical applications in gradient descent, backpropagation, and regression analysis.
What is calculus?
Module 1 · 3 Hours to complete
Multivariate calculus
Module 2 · 3 Hours to complete
Multivariate chain rule and its applications
Module 3 · 3 Hours to complete
Taylor series and linearisation
Module 4 · 2 Hours to complete
Intro to optimisation
Module 5 · 2 Hours to complete
Regression
Module 6 · 2 Hours to complete
Fee Structure
Instructors
Strategic Teaching Fellow in Design Engineering
Dr. Freddie Page serves as the Strategic Teaching Fellow in the Dyson School of Design Engineering at Imperial College London. He earned his MPhys from the University of Oxford in 2011 and completed his PhD in theoretical nanophotonics at Imperial College London in 2016. His research focuses on designing materials capable of slowing light to a complete stop and exploring the interactions of light with sheets of graphene far from thermal equilibrium.
Professor David Dye: Expert in Alloy Development and Materials Science
David Dye is a Professor of Metallurgy in the Department of Materials, specializing in the development of alloys for jet engines, nuclear applications, and caloric materials aimed at reducing fuel consumption and preventing in-service failures. His work involves advanced crystallography, utilizing techniques such as neutron and synchrotron X-ray diffraction and electron microscopy at the atomic scale. The large datasets generated by these techniques present complex data analysis challenges. David earned his PhD and undergraduate degrees from Cambridge University in 1997 and 2000, respectively, and joined Imperial College London in 2003. He also teaches introductory mathematics, with a focus on errors and data analysis, and has been recognized with student-led awards for his innovative teaching methods.
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