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Imperial College London

Mathematics for Machine Learning: Linear Algebra

Imperial College London via Coursera

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Overview

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In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.

At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

Taught by

David Dye, Samuel J. Cooper and A. Freddie Page

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Reviews

2.9 rating, based on 9 Class Central reviews

Start your review of Mathematics for Machine Learning: Linear Algebra

  • Profile image for Sina Rahimian
    Sina Rahimian
    Cons:
    concepts poorly explained.
    Coding assignments had so many problems and internal bugs.

    pros:
    Lots of practical calculations.

    Overall:
    Overrated and not useful for ml specialists.

  • Profile image for András Novoszáth
    András Novoszáth

    András Novoszáth completed this course, spending 5 hours a week on it and found the course difficulty to be easy.

    I studied linear algebra at university, more than 10 years ago so I had some memories of the topic. I also familiar with python. I audited the course to gain practical experience and notation reading skills for my data science studies.

    I liked the concepts and the lecture style. These were well done.

    On the other hand they do not hand out almost any written material helping to understand the concepts and calculation required for the assessments. Accordingly, the assessments of the first week are quite easy but become much harder. Sadly not because the material is that complicated (what they explain, they demonstrate really well) but because they do not cover some parts of it.
  • Profile image for Maria Clarissa Fionalita
    Maria Clarissa Fionalita
    I feel like this course is underrated for people who want to learn machine learning. This, coming from someone who never did engineering degree. From the reviews seem like people were not satisfied with the lectures, but since week 1 they recommende…
  • Ayse N.

    Ayse N. is taking this course right now and found the course difficulty to be very hard.

    I think the idea for the course is great. But I found the way they teach hard to follow. Just from the start, there are many questions that require prior understanding of Statistics, and the answers are not explained well. The content, as much as I have seen, is not very accessible.

    Also for those who consider auditing this course; some videos are not available at all, so this course is not ideal for those who want to take it for free.
  • Anonymous

    Anonymous completed this course.

    I speculate that the aims of the course were to simplify linear algebra to the bare essentials needed for machine learning. Unfortunately, the creators of the course have done it to an excessive extent such that many important concepts were glossed over, leaving the student extremely confused.

    Would not recommend due to its hand-wavy nature. Take a proper linear algebra course if you can.
  • Abdul Hannan

    Abdul Hannan completed this course.

    Its a great course for people trying to learn maths behind ML. I attended Prof Ng course on ML but thought I lack skills behind those algorithms. This course has given me lots of confidence to learn math behind ML. You have to put some efforts and search around the internet where things are not clear in the course.
  • Profile image for Naresh Sharma
    Naresh Sharma

    Naresh Sharma completed this course.

    The lectures on multi-variate calculus is too hand wavy and fast.. very difficult to follow.. as if the instructor is talking to himself. Probably not worth the effort.

    Also only the first week is free... so the free thing is misleading.
  • Benjamin Lau

    Benjamin Lau completed this course, spending 4 hours a week on it and found the course difficulty to be easy.

    Break down all the essential knowledge required in the topic. I actually used this as an introductory course and delve deeper into each topics with other resources
  • Sagar Ladhwani

    Sagar Ladhwani completed this course and found the course difficulty to be very easy.

    Although, pretty elementary (if you are an engineer), the course dives into the geometrical intuition of how vectors operate in space and the physical meaning of the various manipulations applied with matrices. The course material was quite easy to grasp and is for beginners in the area but if you opt for the 3 part specializations that this course belongs to, this one will help in establishing the footwork for the next courses.

    https://www.linkedin.com/posts/sagar-ladhwani-713b96112_mathematics-machinelearning-datascience-activity-6634710999159144448-a5A5
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