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Volver a Mathematics for Machine Learning: Multivariate Calculus

Opiniones y comentarios de aprendices correspondientes a Mathematics for Machine Learning: Multivariate Calculus por parte de Imperial College London

1,504 calificaciones
221 revisiones

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This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future....

Principales revisiones


Nov 26, 2018

Great course to develop some understanding and intuition about the basic concepts used in optimization. Last 2 weeks were a bit on a lower level of quality then the rest in my opinion but still great.


Nov 13, 2018

Excellent course. I completed this course with no prior knowledge of multivariate calculus and was successful nonetheless. It was challenging and extremely interesting, informative, and well designed.

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1 - 25 de 232 revisiones para Mathematics for Machine Learning: Multivariate Calculus

por Tanuj J

Jan 19, 2019

Topics need to be covered more in depth. Too much information packed into this course. Instructor's explanations are also not clear most of the time. It will be hard to follow this course if you don't have some background with calculus.

por Marc P

Apr 28, 2019

The course is led by two instructor and my ratings is an average of the two performances. The videos in week 1 to 4 are absolutely outstanding and a pleasure to follow. The ones in week 5 and 6 are ok but not great. The use of quizzes and coding assignments throughout the course is very engaging and of great use for retention and application of the learned subjects.

por João C L S

Apr 17, 2019

I liked the course specially because I finally understood Backpropagation, an old frustration from Andrew Ng's Machine Learning course. It covers the main topics for Mathematics for Machine Learning as promised. Two weak points: (1) the Newton-Raphson convergence problems, superficially covered in the lectures, but has a challenging test, no forum support, no other source indicated for helping us. (2) The forum is abandoned. I've set two problems, one of them about an error in a lecture and the second about the problem with Newton-Raphson lecture. No responses from the lecturers or mentors.

por Yan

Mar 31, 2019

Some errors confused many students. And they are remained unfixed.

por Andrii S

Jan 20, 2019


por Narayan B

Jun 25, 2019

good mathematics course, but the things and concepts are explained in a very abstract way. Need to think a lot on your own while solving the quizzes as the videos are not going to help. Most of the concepts i learnt were from the quizzes rather than the videos

por prudhvi

Jun 23, 2019

week 6 content is not clear at least for me .

por Mattia P

Jun 22, 2019

Great content, amazing lecturers...just brilliant. Well done Imperial.

por Fish

Jun 21, 2019


por Valeria B

Jun 17, 2019

The first part of the course is fine. Towards the end, lots of interesting concepts explained too quickly. I'd rather have more detailed explanations, especially about linear and non-linear regression.

The examples are quite good.

por Fabian B

Jun 16, 2019

Challenging, but very exciting. Learned a lot interesting fundamentals. Thank you.

por Gautham T

Jun 16, 2019

fantastic course

por Shahan C

Jun 15, 2019

Helped me understand the underlying match concepts in machine learning and did help me professionally.

por phu c d

Jun 11, 2019

The course is very useful with very interesting quizzes and assignments


Jun 10, 2019

That's some excellent course to take for! Awesome explanations for the concepts and I strongly recommend khan academy for further explanations.

por Surinder D

Jun 10, 2019

1.Week 5 should be taken in separate module dedicated to statistics.

2.The duration of course can be increased.

3. Week 3 and week 4 can be made more detailed

por Rishabh A

Jun 09, 2019

Loved the course. Backpropogation section needs more elaborate explanation, where are we doing dot products, where are we doing matrix multiplications, things go confusing.

por Harsh D

Jun 03, 2019

Awesome course, just wish it had more info on hessian.

por Мусаллямов Д Н

May 31, 2019


por Leigh F

May 29, 2019

Concepts not clearly explained.

por Kris S

May 28, 2019

Very well taught, great flow of lectures, awesome teachers!


May 27, 2019

El curso explica de forma sencilla, los conceptos de calculo necesarios para la comprensión del aprendizaje de máquina. Realmente muy útil. Además, incluye ejercicios prácticos en python

por Shuvo D N

May 26, 2019


por Lee j s

May 23, 2019

Too fast to understand what instructors says.. but lecture contents are good

por Phạm N M H

May 23, 2019

This is one of three course in Mathematics for ML, it'll give you intuition for understand the true meaning of ML/DL/AI , it's all about math