Mathematics for Machine Learning: Multivariate Calculus に戻る

4.7

2,168件の評価

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325件のレビュー

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.

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by 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.

by 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.

by 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.

by Nushaine F

•Jul 18, 2019

This is my first time learning calculus (I'm a 16 y/o high-school sophomore), and I'm satisfied with this course. The instructors were great, and the assignments are awesome.

If I would suggest one improvement, it would be to give more examples in the lectures. Some lectures were packed with examples, and some had none at all. I had to often refer to Khan Academy and YouTube to learn the concepts which the instructors did not provide an example for. (Especially in Week 4). Sometimes this would frustrate me because it would take me hours to grasp a concept.

Having said this, this course is for you if: (1) - you want a refresher on fundamental calculus concepts that relate to machine learning, or (2) - if you want to learn calculus for the first time, and you have a strong desire to learn these concepts. But no matter what, DON'T GIVE UP and don't stop until you've completed the course.

I hoped this has helped and good luck on your ML journey!

by Yan

•Mar 31, 2019

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

by Andrii S

•Jan 20, 2019

Excellent.

by James L T

•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.

by Oleg B

•Dec 12, 2018

Excellent summaries of important points.

by 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.

by Jonathan C

•Oct 24, 2019

I don't want to be too hard on this course since I really liked some parts of it. Especially, the instructor in Week 1 - 4 did a good job explaining the concepts and overall one can clearly see that a lot of effort was put into the creation of this course. However, I found that a lot of topics could be handled a lot more in-depth.

The assessment at the end of a week was not really challenging and does not require a deep understanding of the concepts. Some of the quizzes were more challenging but in the assessments it was often only required to answer questions based on graphs or other images of functions. Most of the programming assignments only required the student to fill in some easier blanks.

I still do not know what the Taylor Series Chapter was about. I guess this is an important concept but I was not sure how this relates to machine learning. If you call a course Math for Machine Learning, I would expect that you relate the concepts to Machine Learning.

Maybe, it is just me but I would have been glad if this course had offered more depth and took at least double the amount of time to complete. This would have been more rewarding, as I do not feel that I learned as much as I hoped for when I started this course.

by Carsten H

•Mar 31, 2018

Too many derivatives of pointless functions.

by 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.

by ChaoLin

•Nov 24, 2018

nice course

by Daniel P

•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.

by Saurav B

•Nov 25, 2018

An intuitive introduction to multivariate calculus and its applications in Machine Learning - the perfect course for a budding computer scientist.

by Sergii T

•Nov 25, 2018

Nice Course. It is well structured and is interesting to follow.

by Serge H k

•Dec 12, 2018

I love the assignments. It was fun being able to translate mathematics equations and algorithms into python code

by 馬健原

•Dec 16, 2018

Good Course

by Mani S Z

•Nov 29, 2018

Excellent course

by imran s

•Dec 02, 2018

Well explained and I would say also https://www.mathsisfun.com/algebra/taylor-series.html to get some more details.

by Grigoraș V

•Dec 29, 2018

The professors are great! Wish we had part of such enthusiasm all throughout high-school. I bet people would enjoy math a lot more.

by Sujeeth S R S

•Jan 12, 2019

very good introductory course to Multivariate Calculus

by James A

•Jan 14, 2019

Amazing!

by 刘静怡

•Jan 14, 2019

Thank you! Really nice course!

by Felipe G P

•Jan 16, 2019

Aulas excelentes! Professor conhece bem o que ensina e tem bastante didática! Recomendo!