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Mathematics for Machine Learning: Linear Algebra(으)로 돌아가기

임페리얼 칼리지 런던의 Mathematics for Machine Learning: Linear Algebra 학습자 리뷰 및 피드백

4.7
별점
10,222개의 평가
2,054개의 리뷰

강좌 소개

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....

최상위 리뷰

EC
2019년 9월 9일

Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.

PL
2018년 8월 25일

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.

필터링 기준:

Mathematics for Machine Learning: Linear Algebra의 2,063개 리뷰 중 1976~2000

교육 기관: rishabh t

2020년 5월 5일

Explainations was good but some topics was difficult to get may be due to my basics

교육 기관: Adam R

2018년 11월 16일

Some of the quizzes go beyond what is in the videos and often spent ages on them.

교육 기관: Nicholas K

2018년 4월 20일

Enough gaps that I finished feeling like I really had no idea what was going on.

교육 기관: David R M

2020년 7월 13일

Requires an understanding of python that doesn't seem to be expressed anywhere

교육 기관: Jose C

2019년 12월 19일

I did not see any specific application of what was learned to Machine Learning

교육 기관: Tory M

2020년 9월 3일

All in all this course served as a good refresher for linear algebra.

교육 기관: Gary M F T

2020년 10월 29일

Esta en el idioma inglés. Seria factibles en el idioma español

교육 기관: Alejandro T R

2020년 8월 2일

Really difficult to understand the explanations of the course.

교육 기관: Ayala A

2020년 7월 25일

The course is good but the explanations are not clear enough.

교육 기관: Ninder J

2019년 6월 17일

not well explained...Rather than this go for khan's academy

교육 기관: rajiv k K

2019년 7월 21일

Good for rivision but I will not recommend to beginner.

교육 기관: Omri S

2019년 10월 25일

Good, but a lot of stuff is not explained in detail

교육 기관: สิทธิพร แ

2020년 5월 29일

some lessons don't cover knowledge for assignment

교육 기관: Flávio H P d O

2018년 5월 11일

explanation not very clear

not enought examples

교육 기관: Rosana J B

2021년 3월 1일

muy confuso el sistema de envío de tareas

교육 기관: Hiralal P

2020년 5월 4일

they should provide more examples

교육 기관: Neha K

2018년 10월 9일

The style of teaching is great.

교육 기관: Lieu Z H

2019년 7월 25일

found the course too basic

교육 기관: Jadhav J N J

2020년 3월 2일

Good Teaching

교육 기관: Rafael L A

2020년 7월 9일

challenging

교육 기관: Navya V

2020년 7월 18일

good

교육 기관: Fuad E

2019년 5월 22일

It is a little messy: there are no clear definitions of Vector Space, Normed Vector Space, Euclidean Vector Space. Functions as COS and SIN are used to show basic concepts, orthogonal base, and so on. "Projection" concept always relies on base being orthogonal, projection being under 90 degree (what is 90 degree in vector space?), and space being Euclidean, although it is much simpler and applicable for just Vector Space (space without "norm" defined). Good introductory course for high-school; bad for University. Good for kids who just finished learning Pythagoras Theorem, SIN, COS, and basis of Euclidean geometry. Example of house (with number of rooms which is positive Integer number, and price which is positive Decimal) is not really a vector. Examples of non-Euclidean spaces and their applications in machine learning not provided (geometrical deep learning on graphs for example). Basic course for those completely unfamiliar with what "vector" is. Provided tests in Python are confusing because in the context we write vectors (and "base" vectors which matrix consists from) vertically, and in Python - horizontally. For example, [[1,2],[3,4]] is matrix, but it won't transform base vector [1,0] into [1,2]. This is confusing and should be mentioned before test begins.

Thank you for helping me to recall this knowledge. I finished three weeks; I may need to update review later.

교육 기관: Mirian A

2020년 7월 23일

Course: Definitely target for people that have solid understand of linear Algebra

Professor:

Pluses: Nice and clear voice, nice demeanor, good energy

Minuses: Long and sometimes messy samples presented on the board, not following through with the samples given (changing subjects causing confusion)

Area of improvement: It would make more interesting if would make connection with real life situation where we could make use of the classes. The instruction video made the class appealing because started with an example of a real life situation that could be resolved. It would be wonderful if full course would bring same excitement.

Exercises/Tests:

Pluses: Unfortunately there was no plus on the exercises. I hate to say that was all pretty bad.

Minus: They were confusing. A lot of time did not make connection with what was taught.

Area of improvement : Give explanation of the answers on the test itself and not referring back to the class. Resolving one to one exercise help making sense of the course being studied.

Course overall was not good. I am very glad I did not pay for this class. However I do think if the professor changes a few things he can nail this class same way he nailed the intro.

교육 기관: Matthew L

2020년 4월 8일

I am new to Coursera so I have no idea of what is standard on here. Maybe this course is good relative to other courses on here, I don't know. However I do know that based on my experience I can not recommend paying for a coursera membership to take this course. This course comes with a total of less than 3.5hrs of instructional video. Considering linear algebra is usually taught with ~45 hrs of classroom instruction, this may seem short.. and it is. The course does a good job at explaining things at a conceptual level however it has few worked through example problems. The course uses quizzes and programming assignments as a way of reinforcing skills that you learn however the correct answers to the questions on quizzes are never reviewed. So if you get something wrong you'll never know what you did wrong unless you figure it out for yourself. Also the forums don't seem to be useful at all. If you are lucky another student might reply.

교육 기관: Hendrik V

2020년 7월 23일

The time commitment is not realistic unless you are a math wiz and experienced programmer. Take the timelines and multiply it by at least 5. Videos do an excellent job of presenting theory and application, but there is no supplemental learning material. You can have to find all of that on your own. In general the other students in the course are lost and have no idea what is going on. I recommend that you watch the videos and follow up the subject on something like Khan Academy where you can work through multiple examples. As for the coding part you need to find someone that knows how to code math in python. Would not recommend.