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Volver a Mathematics for Machine Learning: PCA

Opiniones y comentarios de aprendices correspondientes a Mathematics for Machine Learning: PCA por parte de Imperial College London

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This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

Principales reseñas


6 de jul. de 2021

Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.


16 de jul. de 2018

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

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476 - 500 de 719 revisiones para Mathematics for Machine Learning: PCA

por Sharon P

24 de sep. de 2018

Mathematically challenging, but satisfying in the end.

por Paulo Y C

11 de feb. de 2019

great material but explanation are a little bit messy

por Anas E j

19 de jun. de 2022

Thank you for this course , hope to learn more !

por Wd E

21 de feb. de 2021

Good course, but requires mathematical background

por taeha k

27 de jul. de 2019

Good but slightly less deeper than the other two

por Eddery L

24 de may. de 2019

The instructor is great. HW setup sucks though.

por Manish C

6 de may. de 2020

Best course for machine learning enthusiast

por Thijs S

28 de sep. de 2020

The last assignment could use improvement.

por andre w

27 de mar. de 2022

a really good course but also really hard

por J N B P

10 de sep. de 2020

Good for intermediates in linear algebra.

por Romesh M P

16 de ene. de 2020

Too much non-video lectures (lot to read)

por 3047 T

13 de jul. de 2020

The last course could have been better.

por Kailash Y

9 de jul. de 2020

Challenging but in a good way.

por Muhammad F T S

28 de mar. de 2021

this was hard but insightful

por Mark R

22 de ene. de 2019

Good, short, overview of PCA

por Changson O

28 de ene. de 2019

Many errors of homework

por Poomphob S

18 de jun. de 2020

so challenging for me

por Sammy R

25 de dic. de 2019

Needs more details

por Shreyas S S

20 de jun. de 2020

Good Course


28 de ago. de 2020

kinda long

por Egi R T

14 de jul. de 2022


por Raihan N J M

12 de mar. de 2021


por Harrison B

18 de abr. de 2020

Broadly speaking, this is a good course. However, the feeling is that it should be twice as long and with more videos. There is simply not enough instruction to facilitate clear learning and completion of this course is down to an individual's desire to read around and problem solve.

In particular, the programming assignments - whilst not technically difficult, lack clear articulation of expectation, which is compounded by pythons slightly inconvenient handling of matrices. Writing vectorised code which involves 1 x N or N x 1 matrices and transpositions often results in zero marks; with no clue whether the code is wrong, the student has misunderstood the expectation or python is refusing to recognise a N x 1 matrix. This could br helped by including more discriptions about the data sets and the variables being used, as well as the expectation of the output.

There are a lot of positives about this course, the videos are well made and are clear. Excellent supplementary learning if you're doing undergraduate Linear Algebra or other Machine Learning courses; just a bit too cramped for a standalone course (even with the others in the specialisation being well understood). Perhaps a four course could be added to this specialisation for "The Basics of Python for Machine Learning" where a student covers all the relevant coding knowledge?

por Mark P

29 de jul. de 2019

This course had a lot of potential but there were a number of inconsistencies, cut/paste comment bugs, that make it more challenging than it needs to be. The comments in the notebook exercises should be triple-checked with the text above to ensure consistency of variables. Far too often these would be mixed up, or the input/output descriptions would be incorrect. Or the unit test would have different dimensions. Lectures often left out steps - e.g. "because of orthonormal basis, we can simplify and remove a bunch of terms" - how exactly? A extra few seconds of explanations would allow students to follow more closely. Notation in lectures is sloppy - sometimes terms would be missing and then the video would quietly cut to a correction. "j's" and "i's" indices were interchanged frequently making the derivations how to follow. Also, this isn't a course on unit testing - some more tests should be included to help students debug individual functions rather than relying on the final algorithm (e.g. PCA to work). It should be explained why the "1/N" term for XX^T is not necessary even though it's in the lectures. On the plus side, the added written notes were welcome and fairly well done.

por Quek J H

29 de oct. de 2022

This course is interesting and taught me basics of PCA. Nonetheless, the last assignment needs revision by the Instructors of this course. There is even an error in one of the assertion tests! And some cells cannot be executed because of some Python syntax or traceback errors. Although these cells are not run by the Autograder, you nontheless cannot see the complete output the notebook is trying to show you. Lastly, in the error messages printe by the Autograder, I cannot seem to find which function is giving me the error! In a previous course by another University with similar programming assignment, the Autograder will point to the erroneous function and shout where went wrong.

I can expect students who have little Python programming to struggle greatly with this course - especially the last assignment. However I believe once the errors are fixed in the notebook, students may have a better experience completing it. I have completed many Coursera courses and I studied Statistics - even I got confused by the last assignment! I believe many students share this sentiment as well.