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Learner Reviews & Feedback for Mathematics for Machine Learning: PCA by Imperial College London

4.0
stars
2,155 ratings
532 reviews

About the Course

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....

Top reviews

JS

Jul 17, 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.

NS

Jun 19, 2020

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

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376 - 400 of 528 Reviews for Mathematics for Machine Learning: PCA

By Toan L T

Oct 03, 2018

Thank you to all the professors and staffs for such a wonderful program. I did learn a lot.

This last course is indeed a fun and challenging one. But it fells short compared to the other two due to some aspects which can be improved in the future.

Nevertheless, I'm glad that I can learn about PCA.

By Ankit C

Apr 19, 2020

The course contents were good, but I felt the explanation was not so clear. Since PCA is a very important topic in Machine Learning, after explaining some new concept, the instructor could've solved a couple of examples with it, so that the newly registered concepts would be crystal clear.

By Gautam K

Jun 24, 2020

Course content is very awesome. The instructor also teaches in a very splendid manner which makes it very easily understandable. But the evaluation method for practice exercise is very worse. Code get stuck for hours. It's been very frustrating waiting for code to get compiled.

By Arnaud J

Jun 12, 2018

This course is way more brutal than the two previous courses in the specializationIt is also very mathematically oriented, it lacks the graphics / animation / intuition that was given in the first two courses.However, if you make it, you indeed have a good understanding of PCA.

By Philipp A R

Mar 06, 2020

A lot of input in relatively short time, main points could be pointed out better in the videos. Assignments were tough but manageable, the instructions could be clearer and more detailed. However, being pushed to figure out things by yourself is also a learning opportunity.

By Xin W

Nov 12, 2019

To me, the first 3 weeks in this course is good. But the 4th week is quite confusing. And I don't understand the applicable meaning for the materials in the 4th week. I may need to review what I learned in the 4th week and then decide whether I understand it completely.

By Manju S

Jan 29, 2019

Good stuff:

Instructor has good knowledge of the subject. The course content structure is designed well.

Bad stuff:

Concepts could have been presented with more clarity. Programming assignments need more instructions and less assumption on what the students already know.

By Gabriel C

Apr 24, 2020

Quality of the course is great, but I would question whether it belongs in this specialization given the huge jump in expected knowledge from the first two courses to this one. Relied alot on the forums and YouTube to gain sufficient knowledge to complete this course.

By Hong L

Apr 27, 2020

The content is decent but there are some bugs in the programming assignments. Particularly the last two programming assignments. The auto-grader for the second to the last assignment passes in some input that is not of the correct form.

By V K

Jul 23, 2020

The course content was very good,but the assignments were harder as knowledge of python libraries was required. It would be very helpful if you change the assignments as I feel the course should rather be about math than python

By Pierre

Apr 10, 2020

Positive points: At the end of the module, you get a good understanding on how PCA works. It fulfill its objective.

Negative points: The assignements are poorly directed, the material is not always clearly explained.

By Alexander Z

Sep 14, 2018

Good Course, but

Too less examples to do the quizes on the first run.

Programming assignments are not clearly stated, so you need unnecessary much time to succeed.

I liked the Linear Algebra & Multivariate Modul more!

By devansh v

Apr 03, 2020

The course is Satisfactory.The content is Good,no doubt about it,but many topics(both mathematical and computational) were unknown and coding assignments of Jupyter notebooks of this course(PCA) are very Buggy

By Norah

Aug 25, 2020

Kinda complicated but doable. The stuff do not monitor the discussion forums unfortunately. Without Susan's detailed & well informative replies I won't be able to complete the course. Big THANK YOU to Susan.

By Marina P

Sep 06, 2019

The course is interesting, but some of the quizzes were not done very well. After the first 2 parts of this course, which were just amazing, this one seems kind of worse, although by itself its not that bad.

By Chad K

Jul 08, 2020

Difficult course. They need more formal tutorials to help with the gap between the videos and the tests and projects. I found it very helpful to buy the instructor's textbook and read along in it.

By Cécile L

Apr 14, 2019

Amazing topic, great teachers and nice videos, but assignments can be slightly frustrating and some aspects (matrix calculus, derivatives, etc.) are really expedited... Still worth your time!!!

By Nicholas K

Apr 28, 2018

It's a shame. There's lots of good material and I learned a lot. But a staggering amount of time was wasted figuring out gaps in the instructions - portions felt more like hazing than teaching.

By Adrian C

Sep 22, 2019

The derivatiion of the PCA in the last week can be broken into 2 weeks with different programming assignments to get a closer and more confident understanding of the PCA method.

By Jean D D S

Aug 31, 2019

I would ask the lecturer to go on more detail on the explanations and do (more) examples.

The lecturer tends to skip a few steps during calculations and demonstrations.

By Dominique D

Jul 22, 2020

The week 2 code was more difficult than the other weeks. The forums are no longer attended by the professors. The access to materials from IC is great.

By Wang Z

Jul 08, 2018

The knowledge introduced in this course is really helpful. However, the programming assignments are very time consuming and not necessarily relevent

By Iurii S

Mar 26, 2018

Decent explanations of PCA idea, but assignments do not provide a clear feedback of what is wrong with the implementation util you get it right.

By w w

Aug 27, 2020

this is a great course except the assignment has quite a few bugs and the videos are too short and lack many topics, and the quiz are too short

By zohair a b

Jun 16, 2020

The First 2 courses of this specialization were very good. I really wish the instructor for this course went into a little more depth.