Chevron Left
返回到 Mathematics for Machine Learning: Linear Algebra

学生对 Imperial College London 提供的 Mathematics for Machine Learning: Linear Algebra 的评价和反馈

4.7
12,541 个评分

课程概述

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....

热门审阅

GB

Aug 16, 2020

The instruction was good throughout, but I would urge fellow students to take the time to work through the problems as suggested. Also, the eigen- stuff is quite tricky and can fool you. Be careful.

LK

Oct 26, 2023

Very good course. I liked very much the way the topics were presented and explained. I especially appreciate David Dye's clarity of explanations, enthusiasm, passion, and joyful attitude. Thank you.

筛选依据:

2376 - Mathematics for Machine Learning: Linear Algebra 的 2400 个评论(共 2,478 个)

创建者 David R M

Jul 13, 2020

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

创建者 Jose H C

Dec 19, 2019

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

创建者 Thomas K

May 4, 2022

Covered topics sup up useful framework that give robust starting point

创建者 Tory M

Sep 3, 2020

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

创建者 Gary M F T

Oct 28, 2020

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

创建者 Alejandro T R

Aug 2, 2020

Really difficult to understand the explanations of the course.

创建者 Ayala A

Jul 24, 2020

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

创建者 Akshat B

May 18, 2023

The content is good, but the support could have been better.

创建者 Ninder J

Jun 17, 2019

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

创建者 rajiv k K

Jul 21, 2019

Good for rivision but I will not recommend to beginner.

创建者 omri s

Oct 25, 2019

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

创建者 สิทธิพร แ

May 29, 2020

some lessons don't cover knowledge for assignment

创建者 Flávio H P d O

May 11, 2018

explanation not very clear

not enought examples

创建者 Rosana J B

Mar 1, 2021

muy confuso el sistema de envío de tareas

创建者 Hiralal P

May 4, 2020

they should provide more examples

创建者 Neha K

Oct 9, 2018

The style of teaching is great.

创建者 Lieu Z H

Jul 25, 2019

found the course too basic

创建者 Jadhav J N J

Mar 2, 2020

Good Teaching

创建者 Néstor E S

Sep 1, 2025

no es bueno

创建者 Rafael L A

Jul 9, 2020

challenging

创建者 Navya V

Jul 18, 2020

good

创建者 Sakshi T

Jan 28, 2023

NA

创建者 Fuad E

May 22, 2019

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.

创建者 c w

Dec 28, 2022

sometimes David just keeps talking about some underlying relationships between the concepts, or doing a bunch of operations, while he writes nothing down or just not provide any visual representation. when he does that its very hard to follow, and i have to replay the same video 10x trying to visualize what he is talking about in my head.

the biggest issue is that throughout this whole course (atleast by week 4 that im on so far), is David did not go through any real world problem examples except for a very simplistic example about store prices, which happens to be the same example that is used by every other teacher. seeing more examples would've definitely added more value.. since many of us are learning this in prep for real applications like data science.

the university should've also put in more effort to create computer generated animations and visual representations of everything that is being taught. Linear algebra is the one of the subjects that requires the most visual intuition, so whoever decided to skip the effort on this should be fired.

that being said, i do appreciate that David tries to explain the concepts in depth and connect the dots between them. this has still been a valuable learning experience to me

创建者 Mirian A

Jul 23, 2020

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.