About this Course
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Beginner Level

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

Approx. 22 hours to complete

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

English

Subtitles: English

Learners taking this Course are

  • Machine Learning Engineers
  • Data Scientists
  • Biostatisticians
  • Data Analysts
  • Software Engineers

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
Motivations for linear algebra3m
Getting a handle on vectors9m
Operations with vectors11m
Summary1m
4 readings
About Imperial College & the team5m
How to be successful in this course5m
Grading policy5m
Additional readings & helpful references10m
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
Modulus & inner product10m
Cosine & dot product5m
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
How matrices transform space5m
Types of matrix transformation8m
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
Matrices changing basis11m
Doing a transformation in a changed basis4m
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
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Top reviews from Mathematics for Machine Learning: Linear Algebra

By NSDec 23rd 2018

Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.

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.

Instructors

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David Dye

Professor of Metallurgy
Department of Materials
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Samuel J. Cooper

Lecturer
Dyson School of Design Engineering
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A. Freddie Page

Strategic Teaching Fellow
Dyson School of Design Engineering

About Imperial College London

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 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....
Mathematics for Machine Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.