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100% online

Start instantly and learn at your own schedule.

Approx. 22 hours to complete

Suggested: 5 weeks of study, 2-5 hours/week...

English

Subtitles: English

Skills you will gain

Eigenvalues And EigenvectorsBasis (Linear Algebra)Transformation MatrixLinear Algebra

100% online

Start instantly and learn at your own schedule.

Approx. 22 hours to complete

Suggested: 5 weeks of study, 2-5 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Introduction to Linear Algebra and to Mathematics for Machine Learning

5 videos (Total 28 min), 4 readings, 3 quizzes
5 videos
Operations with vectors11m
Summary1m
About Imperial College & the team5m
How to be successful in this course5m
3 practice exercises
Exploring parameter space20m
Solving some simultaneous equations15m
Doing some vector operations14m
Week
2
2 hours to complete

Vectors are objects that move around space

8 videos (Total 44 min), 4 quizzes
8 videos
Projection6m
Changing basis11m
Basis, vector space, and linear independence4m
Applications of changing basis3m
Summary1m
4 practice exercises
Dot product of vectors15m
Changing basis15m
Linear dependency of a set of vectors15m
Vector operations assessment15m
Week
3
3 hours to complete

Matrices in Linear Algebra: Objects that operate on Vectors

8 videos (Total 57 min), 3 quizzes
8 videos
Composition or combination of matrix transformations8m
Solving the apples and bananas problem: Gaussian elimination8m
Going from Gaussian elimination to finding the inverse matrix8m
Determinants and inverses10m
Summary59s
2 practice exercises
Using matrices to make transformations12m
Solving linear equations using the inverse matrix16m
Week
4
6 hours to complete

Matrices make linear mappings

6 videos (Total 53 min), 4 quizzes
6 videos
Orthogonal matrices6m
The Gram–Schmidt process6m
Example: Reflecting in a plane14m
2 practice exercises
Non-square matrix multiplication20m
Example: Using non-square matrices to do a projection12m
4.7
636 Reviews

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started a new career after completing these courses

32%

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Top reviews from Mathematics for Machine Learning: Linear Algebra

By PLAug 26th 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

By CSApr 1st 2018

Amazing course, great instructors. The amount of working linear algebra knowledge you get from this single course is substantial. It has already helped solidify my learning in other ML and AI courses.

Instructors

David Dye

Professor of Metallurgy
Department of Materials

Samuel J. Cooper

Lecturer
Dyson School of Design Engineering

A. Freddie Page

Strategic Teaching Fellow
Dyson School of Design Engineering

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology....

About the Mathematics for Machine Learning Specialization

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require basic Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning....