Mathematics for Machine Learning
Linear Algebra
Below are the top discussions from Reddit that mention this online Coursera course from Imperial College London.
Offered by Imperial College London. In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and ... Enroll for free.
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Taught by
David Dye
Professor of Metallurgy
and 2 more instructors
Offered by
Imperial College London
Reddit Posts and Comments
0 posts • 29 mentions • top 25 shown below
13 points • theritznl
I have oriented on this question quite a bit and in general math is not used that much. It is when you’re more at the algorithm/deep learning side of things. It is however useful to have a grasp on some algebra, calculus and linear regression in particular. So I’m going to do these courses:
 [ ] Algebra https://www.coursera.org/learn/datasciencemathskills
 [ ] Lineair https://www.coursera.org/learn/linearalgebramachinelearning or https://github.com/fastai/numericallinearalgebra/blob/master/README.md
 [ ] Calculus https://www.edx.org/course/preuniversitycalculus or https://www.coursera.org/learn/multivariatecalculusmachinelearning of easy way out —> http://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
Statistics is more useful to read up on.
8 points • cb_hanson_III
Don't listen to this guy. He's coming at this from a math or stem major perspective. As an applied person, the important thing is you have the concepts. Most of the heavy calculations will be done in computer code. You most definitely don't need to approach Linear Algebra from the perspective of proving things. (This is more to cater to math majors who need to learn proofs and need a more gentle introduction to proofs than real analysis. Many universities provide this in their linear algebra course. Don't worry too much about this. You are an applied researcher.)
To give a good (but short) introduction, I would look at a course like this one from Imperial College https://www.coursera.org/learn/linearalgebramachinelearning?specialization=mathematicsmachinelearning. No proofs, nothing beyond adding and subtraction, no complex numbers. But you still get the key intuition behind vectors, projections, transformations, eigenvectors, etc.
If you think this is too easy, then look at Strang's Linear Algebra at MIT. https://ocw.mit.edu/courses/mathematics/1806linearalgebraspring2010/ It's a much more complete course with an emphasis on basic understanding and nothing too abstract. Not much in the way of proofs and only algebra required for most of the topics.
8 points • MysticMania
Like the other commenter mentioned, there are a ton of college courses that you can take online.
Looks like Coursera has a Linear Algebra course that happens to start today :D https://www.coursera.org/learn/linearalgebramachinelearning
​
I also like Udacity because most of their courses are free, the videos are literally hosted on youtube, and they like to go indepth on a lot of topics. I did their DS/Algos course a while back, it was a good starting point for my last job search since it covered the basics well.
Looks like they has a free Linear Algebra Refresher course although I haven't taken this course myself: https://www.udacity.com/course/linearalgebrarefreshercourseud953
1 points • ratterstinkle
+1 for the Imperial College of London Coursera course.
1 points • gtani
there's lots of courses and meetups also for data science just google around https://www.coursera.org/learn/linearalgebramachinelearning
1 points • hephaestus29
Regarding Linear Algebra, I would recommend a course on Coursera https://www.coursera.org/learn/linearalgebramachinelearning. I had taken this one prior to getting started on my long Machine Learning voyage. The concepts related to vectors and matrices have been explained elaborately giving you an intuition behind each topic, and starting right from the basics.
1 points • synthphreak
The course I provided will teach you how to operate on matrices of numbers using numpy
, which is exactly what the title of your post requested. That involves linear algebra though it’s obviously much more than that.
If you want only the math without any focus on data analysis, check this one out. Though it’s obviously focused specifically on Machine Learning applications, which may or may not be what you want.
Also, tone down the ‘tude next time. People are just trying to help you and don’t owe you shit.
3 points • Apathiq
I think there are 3 linear algebra topics you should understand in order to get what a PCA is doing:
 Matrix as linear transformations
 Change of basis between different vector spaces
 Eigenvectors and Eigenvalues
For the basic ideas you could take a look into this nice coursera course: https://www.coursera.org/learn/linearalgebramachinelearning?specialization=mathematicsmachinelearning
But my recommendation would be taking a linear algebra course/book if you have the time and really want to go beyond "A PCA rotates your data in a way that a few dimensions/variables of the new dataset represent most of the variance found in the original one".
11 points • 7___7
https://www.khanacademy.org/math/linearalgebra
https://www.coursera.org/learn/linearalgebramachinelearning
https://www.edx.org/course/linearequationspart1
https://www.coursera.org/specializations/algorithms#courses
4 points • highlyquestionabl
Advice for a mathematical moron
I am interested in learning Machine Learning as a hobby and, maybe, in the distant future as a career. The problem is, I have a graduate degree in a totally unrelated field and am a dunce when it comes to math.
I read the Super Harsh Guide and quickly realized that Elements is well out of my depth, so I began reading the (apparently) easier Introduction to Statistical Learning; the material covered within is still somewhat beyond me. Are there any suggestions as to where to start for someone who knows very little math beyond basic introductory algebra? I know it's a big ask and I'm aware that I'll likely never work at Google Brain, however I'm really interested in the topic and would like to become more educated for my own personal satisfaction.
I have been looking at the Intro to Probability and Data course for introductory statistics and the Mathematics for Machine Learning: Linear algebra and Calculus courses for general math. Do these seem sufficient for getting into the Intro book? Contrarily, is this overkill/should I just read the Introduction to Stat Learning book and glean as much as I can without any prep? Will I even be able to understand these courses with only a basic algebra background?
I know this is a text dump; thanks for reading and please know that any insight is much appreciated.
2 points • pizza_e_ketchup
Relaxa, assim como qualquer matéria, só estudar. Hoje em dia ainda é muito mais fácil. Os recursos computacionais disponíveis facilitam demais entender os conceitos. Em relação a isso, sugiro ir dando uma olhada no canal 3blue1brown [1] a medida que você for estudando as coisas na faculdade, daí você vai conseguir "ver" as equações. Além disso, tem um curso de álgebra linear para machine learning no coursera que vale muito a pena como complemento do seu curso principal [2].
[1] https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw
[2] https://www.coursera.org/learn/linearalgebramachinelearning/
2 points • ghjm
There's Chris Pryby's "Linear Algebra Refresher" course that was at one time being recommended to OMSCS students (and maybe still is): https://www.udacity.com/course/linearalgebrarefreshercourseud953
I took this and didn't think it was very good. I've had my eye on this: https://www.coursera.org/learn/linearalgebramachinelearning but have not taken it so can't comment on its quality.
If you mean accredited courses for credit, I haven't looked.
3 points • rmc0d3r
The Deep Learning Book has a part on the linear algebra required to understand deep learning.
http://www.deeplearningbook.org/contents/linear_algebra.html
There is also a github page that follows the book closely with regard to linear algebra and provides some codes,
https://hadrienj.github.io/posts/DeepLearningBookSeriesIntroduction/
I also came across a Coursera course titled ‘Mathematics for machine learning: Linear Algebra’.
https://www.coursera.org/learn/linearalgebramachinelearning
1 points • myristicae
https://www.coursera.org/learn/linearalgebramachinelearning
This course is a nice intro and it's focused on what you actually need as a programmer (specifically for machine learning). The projects involve programming too (Python) so it ties things together nicely. I'm not a math person and I liked it. I thought the calculus one (the next in the series) was good too.
1 points • ktkttn_hat
This coursera course looks pretty good: https://www.coursera.org/learn/linearalgebramachinelearning#syllabus. If you liked your computational methods class, machine learning might be interesting for you!
Not a direct answer to your question but from your username, I suspect you might have an intro to QM textbook around. It will likely have an intro chapter on "the math needed for this textbook" which should give you some intuition on how linear algebra can be used as a descriptive system, or framework, for theory.
3 points • mallasahaj
This is what I did when I started getting into maths for machine learning.
1) Mathematics for Machine Learning: Linear Algebra coupled with 3b1b: Essence of linear algebra
Then
2) 3b1b: Essence of Calculus, Mathematics for Machine Learning: Multivariate Calculus and Khan Academy: Multivariate Calculus by 3b1b
After that, I did Andrew ng's machine learning and with all the understanding of mathematics in my head, this course turned out to be very easy.
Then after,
3) I learned Numpy, Pandas and did Mathematics for Machine Learning: PCA
I'm doing deep learning specialization and all these maths are helping me understand faster.
I suggest you, do the maths first and then the algorithms. You'll thank yourself later!
Good luck!
3 points • FirstBabyChancellor
Is it just the linear algebra you're struggling with? Because that's really only 1015% of CSE 6040, basicaly the numpy sections. The rest is just python and learning how to use the language and its relevant libraries (pandas and numpy).
I took CSE 6040 before ISYE 6501 and even though I didn't have the background for some of the things we were doing (e.g., implementing PCA in python without understanding what PCA actually was), I still had very few problems with the course and found it relatively easy because I'm generally comfortable with python. Admittedly, I have familiarity with linear algebra from my undergrad years, so while I also found the numpy sections harder, I could still understand what's going on once I saw the solution and it easily 'clicked'.
If python/programming is your problem in general, then that's not necessarily good news because there's a lot of it in the program. But if you found ISYE 6501 easy, that also has a good amount of coding so I don't know...
If not understanding the concepts behind the programming is an issue, remember that CSE 6040's focus is more on building your programming proficiency than with teaching you the mathematical concepts (like I said, I implemented PCA with ease without understand what it actually meant).
If the linear algebra alone is the problem, then...mixed news for you. There are some courses, like Deterministic Optimisation, High Dimensional Data Analytics, Computational Data Analytics, which all require linear algebra knowledge as a prerequisite. There are probably a few others I haven't mentioned. You'd struggle with these. On the other hand, none of these are really compulsory (ISYE 6740 CDA is, but only if you're taking the computational analytics track) so you can avoid them and take courses without significant knowledge of linear algebra if you want, though linear algebra really does form the foundation of a lot of data science because of its ability to quickly process information without running fullblown for loops so you'll likely encounter it again in different places.
It's just knowledge like any other, though. I've found that linear algebra is often taught in an obtuse manner, where the focus is more on the mechanical operations than the actual intuition and meaning behind those operations (e.g., how to find the inverse of a matrix instead of what the inverse means and why you'd want to use it in solving real problems). I've found that once you understand that intuition, linear algebra is generally kinda easy, but if it's not taught properly and all you learn is those mechanical operations and how to do them, you can easily start getting headaches.
But there are good courses that can help you get up to speed. Some of these are:
 Essence of Linear Algebra by 3blue1brown
 Mathematics for Machine Learning Coursera Specialisation
 Mathematics for Machine Learning textbook & GitHub Companion Site
 Linear Algebra: Foundations to Frontiers
 Gilbert Strang's Linear Algebra lectures at MIT
I'd recommend taking them in that order. The 3blue1brown videos will give you the intuition for what different LA operations are all about and the Coursera specialisation will reinforce this and teach how to compute (and program) them. Then, go back to the 3blue1brown videos to reinforce that intuition of what all those mechanical operations you learned to perform are actually useful for. If you do just these two, you should have at least a good foundation for OMSA. You can refer to the textbook if you want as well to build out your knowledge more formally.
If you want to learn even more/with more academic rigour, the LAFF courses have an excellent reputation and there are three followon courses for even more advanced linear algebra if you want to do that. Alternatively, you can also follow the MIT lectures. Gilbert Strang is the author of one of the most popular LA textbooks out there, so you know you're in good hands.
3 points • bresilient
Free resources:
 videos in https://www2.isye.gatech.edu/\~sman/courses/6739/

https://www.3blue1brown.com/topics/linearalgebra
 The first 150 pages of Ian Goodfellow's Deep Learning book
1 points • melevine45
Accepted:
Received recommendation for admission on 4/4 and an official offer on 4/9. Accepted on 4/12.
 Undergrad: top 50 school, 3.95 GPA, Economics
 Work: Data Analyst at financial technology firm, 6+ years
 For prereqs:
Got an A in NYU Tandon's Bridge to Computer Science Program: (https://engineering.nyu.edu/academics/programs/bridgeprogramnyutandon/computerscience)
Completed the MOOC from ICL: Mathematics for Machine Learning: Linear Algebra (https://www.coursera.org/learn/linearalgebramachinelearning)
 Contributed to C++ open source projects on github
1 points • LiberalTexanGuy
If you don't need accredited coursework, you can audit these for free. They're pretty good and cover most of the math you'll encounter in ML courses:
https://www.coursera.org/learn/linearalgebramachinelearning
https://www.coursera.org/learn/multivariatecalculusmachinelearning