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返回到 Mathematics for Machine Learning: Multivariate Calculus

学生对 伦敦帝国学院 提供的 Mathematics for Machine Learning: Multivariate Calculus 的评价和反馈

3,425 个评分
580 条评论


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....



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.


Aug 04, 2019

Very Well Explained. Good content and great explanation of content. Complex topics are also covered in very easy way. Very Helpful for learning much more complex topics for Machine Learning in future.


26 - Mathematics for Machine Learning: Multivariate Calculus 的 50 个评论(共 584 个)

创建者 Samresh

Aug 04, 2019

Very Well Explained. Good content and great explanation of content. Complex topics are also covered in very easy way. Very Helpful for learning much more complex topics for Machine Learning in future.

创建者 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.

创建者 Benjamin F

Nov 01, 2019

Relevant content. Great instructions. Likable instructors. Very bad coding assignments.

创建者 Ong J R

Jul 23, 2018

Course videos and quizzes are good and content is clearly explained. However, too many concepts are covered with too little depth. For example least squares and non-linear least squares involve fundamental concepts that should be covered and alone, would at least 2 weeks to teach. Lagrange multipliers and Taylor series are barely introduced with very little mathematical derivation involved. I had the impression that I would learn more mathematical theory than machine learning in this course, it didn't turn out to be so.

创建者 Oliverio J S J

May 26, 2020

This mathematics for machine learning course is not a mathematics course. It starts well, explaining mathematical concepts and, suddenly: neural networks, python programming, numpy, scikit... The speed at which the concepts are explained makes it impossible to assimilate anything unless you already know the concepts beforehand, which means this course only serves as a refresher course.

创建者 Mahwish A

Apr 26, 2020

Second professor David is waste of time while the first one is excellent.

创建者 Carsten H

Mar 31, 2018

Too many derivatives of pointless functions.

创建者 Kaustubh L

Jul 14, 2020

It's great, however if you are hoping that they would teach you to differentiate like teachers in high school then you are in the wrong place. But, if you want to build an intuition about calculus, optimization techniques, neural networks then you are in the right place. Personally, I was good at calculus in school so it was relatively easy for me, but if that's not the case for you I would recommend that you brush up your basic differentiation. Also basic knowledge of python numpy library would be super useful. Also this course will introduce some really scary looking formulas, so don't be intimidated they just look scary. Best of Luck !

创建者 Jaiber J

Apr 17, 2020

Simply excellent course. The breadth of topics one needs to cover is astounding. I liked the way the topics and ordered, and following a common structure. The best part is the assignments - one really needs to understand every word of what the instructor says to solve it. They are tough in general to anyone who's done their bachelors/masters long time ago. For those who are not used to programming, the assignments can be difficult.

创建者 Onkar A

May 20, 2020

Awesome course, so much to learn, and all concepts built up from basic, had fun with all assignments and stand-pit like interactive things, really boosted the understanding, i felt that prof. copper's speed of teaching was fast for me persoanlly , i had to pause many times and think what he said, but prof. david's pace was perfect for me, both instructors are great!

创建者 Douglas W

May 04, 2020

Such an impressive set of instructors! I loved the enthusiasm at which the material was taught and injections of British humor. Now, this is not an easy course, but one that requires work. Plan on reacquainting yourselves with pencil, paper and practice. So, do the work, repeat videos when required, rely on classmates in the forums and you'll do fine.

创建者 Idris R

Oct 28, 2019

Fun and challenging course! It's priceless to learn all the math behind neural networks and other machine learning algorithms without having to learn all of calculus and all of linear algebra. Those are large fields and having the material presented in a way that focuses on the most relevant pieces is hugely valuable.

创建者 Agamjyot S C

Jun 16, 2020

This is a must take course, if you want an insight into how the world of machine learning really works. This MOOC focused more on the intuition rather than just deriving out expressions for the heck of it. Everything has been explained in a very nice and simple manner, I have learned a great deal from this course.

创建者 Xin Y

Apr 16, 2020

Excellent course! All concepts are clearly explained. On a difficulty scale of 10 with 0 being easiest and 10 being hardest, quizzes are around 4 and programming assignments are around 1. The lectures are very well designed, with quizzes helping to reinforce key concepts you learned in the videos.

创建者 Nikesh K

Apr 16, 2020

I loved this course. Instead of going deep into mathematical terms, the professors try to create an intuition of those terms and the geometrical explanations further improve the understanding. So if you feel that you lack an understanding of the concepts then definitely go for this specialization

创建者 anurag

Apr 08, 2020

Great foundation course on maths involved in Machine learning . Also such a refreshing view on calculus that is taught in bachelor degrees of engineering. I was able to visualize each concept taught here thus making the learning much more fun and easy to remember, thanks to the professors.

创建者 Nguyen N D

Jul 13, 2020

I learned a lot from the materials. I found the programming assignments in every module is really fascinating with some really cool outputs! The instructors had smooth explanations, easy to understand. But one thing to improve is to have more examples of its applications in real life.

创建者 Renato

May 01, 2020

The class is great, give you the fundamentals of multivariate Calculus, made me remember a lot and I have learnt a lot as well. A lot of work with you do everything yourself but it is fun specially because they mix the math on the paper and the math on the coding (python) really fun.

创建者 Lay K L

Dec 27, 2019

The focus on building intuition about why were are using a certain technique to approach a certain problem, instead of grinding on endless calculus problems in a traditional undergraduate class is very helpful for learning quickly - the class covers a lot of material in a short time.

创建者 Mikhail D

May 24, 2020

As good as the linear algebra course. Lots of important concept in calculus explained in a visual, intuitive way, without bothering the students with too much detail but making sure that they actually understand the concepts well. Both instructors and great and passionate lecturers.

创建者 Arka S

May 27, 2020

A great course. Engaging videos, understandable and solvable quizzes and assignments, and enthusiastic instructors. Gained a lot of insight into the topic, and this is coming from a person who has done Multivariate Calculus in University. Loved the part on Taylor's theorem.

创建者 Rob O

May 31, 2020

This is an excellent refresher course on differential calculus emphasizing applications to machine learning. Exercises and quizzes are a mixture of solving problems by hand and completing Python coding challenges. I found this course to be very effective and worthwhile.

创建者 Niju M N

Mar 26, 2020

Multivariate Calculus - part of Math for machine learning is a good course to brush up your math skills or to learn the basics of Calculus behind ML. Its a introductory course that helps to understand Taylor series, Jacobians , Hessians. OverAll this is a good time spent

创建者 Kuldeep J

Aug 25, 2019

All the mathematical constructs and deep calculus was explained in a very intuitively with the help of visually rich animations. It seems the course content creators have spent good amount of effort in creating animations for every little useful thing, kudos to them.

创建者 Bryan S

Feb 19, 2019

I began this course without any knowledge of calculus and I was still able to get along decently well. I did a bit of supplementary work using Khan Academy but that was more to ingrain the calculus knowledge gained (product rule, chain rule, etc) within this course .