# 學生對 伦敦帝国学院 提供的 Mathematics for Machine Learning: PCA 的評價和反饋

4.0
2,101 個評分
516 條評論

## 課程概述

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....

## 熱門審閱

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.

## 51 - Mathematics for Machine Learning: PCA 的 75 個評論（共 513 個）

Jun 27, 2019

Not as good as previous two courses. I understand it is an intermediate course, but still, the video does not help you do the quiz, e.g. the video uses 2x2 matrices for example while quiz is mainly about 3x3 - then why not include a 3x3 example? Programming assignment is not clear either, some places you have to change the shape of matrix but it is not explained why this is necessary (and actually it is not). A lot of room for improvement here.

May 17, 2020

Very challenging course in terms of computing ; one have to always go to the forum which is very active and function like StackOverFlow. You must have somme skills in PYthon, an intermediate level in matrix algebra and deserve a high amount of time and effort to do the assignments but at the end you get a good comprehension of PCA algorithm.

May 28, 2019

Unfortunately, mistakes in grader and a bad python environment spoilt the impression. I lose hours because of it during 1, 2 and 4 week. It's not enough exercises last week. You should add more examples for every step of PCA for better understanding.

Jun 03, 2020

The course is relatively harder than the other two courses in this specialization. The lecturer didn't provide understandable examples while demonstrating the concepts. The grader for Python assignments didn't function well either.

Mar 28, 2019

The teaching is good but some programming assignment is not so good

Jun 29, 2019

Confusing for beginners; the explanations are too abrupt

Jul 22, 2020

This course was very frustrating. I would say that I'm quite competent in math, but I still struggled, not necessarily because the content is challenging, but because the instructions are unclear. I like that the lectures go through derivations in detail, but the instructor often skips steps. Sometimes he would reference a property of matrices that were not talked about, and I would have to spend half an hour researching what that property was to follow what was happening. The quizzes were minimally helpful, as they were merely the same computation question repeated throughout the quiz, which does not help to build intuitive understanding. The programming assignments are unclear on instructions and had many bugs, even in the pre-written parts. A lot of time was spent on reading the NumPy documentation, as the assignments gave little indication of what functions should be used and how they should be used. Overall, despite having a mathematical derivation of PCA, the course is very confusing and frustrating, perhaps even to those competent in this area of study.

May 26, 2020

Although the lecturer admits that the course is quite challenging at times, it is a poor justification for the terrible assignments with close to zero explanations, errors in functions and lots of misfunctioning code in general where the notebook keeps spinning in an infinite loop. I was very hesitant while rating this course - sometimes I wanted to give it 4 stars and sometimes just a single one. I ended up with just two due to the really bad final programming assignment.

May 30, 2020

Pretty bad in comparison to the previous 2 courses. Not sure if the topic was just harder or it was presented less clearly. Assignments were confusing and I spent a lot of time trying to work out what I was supposed to be doing. More relevant practice questions might have been better. Also course felt slightly detached and maybe collaboration between the tutors which seemed to be there in the previous course should have happened here.

Apr 18, 2019

Not really well structured. Too much in-depth details, too little intuition given. Didn't help to understand PCA. Had to constantly look for other resources online. Pity, because first 2 courses in the specialisation were really good.

Jun 03, 2020

the instructor is knowledgeable but he has no teaching skills what so ever. He makes things very confusing. An example at the end would be very useful. No step-wise algorithm is provided.

Feb 01, 2019

Nov 01, 2018

terrible assignments

Jun 13, 2020

The first two courses in the specialization were extremely good. However, this course is nowhere close to them. Neither does the instructor provide enough intuition, nor do the assignments help in the learning process.

Jun 07, 2020

The first two courses of this specialisation were awesome PCA being a hard topic is difficult to understand but the course was boring and not good compared to previous two.

Jan 30, 2019

Programming assignments' quality is too bad to follow it. Their lecture's explanation and assignments' notation are not matched. Moreover, the code is sometimes ridiculous.

Nov 18, 2019

The didactic value of this course is rather low. The lectures do not explain the very concepts required to sovle the subsequent assigments, or do it in a very poor way.

May 18, 2020

Worst Course I have ever token on Coursera, the instructor hadn't mention any examples or simplify the information.

Jun 12, 2020

Very tough course, the first 3 weeks are good, but the last week is as poorly explained as one can imagine

May 16, 2020

If I could give it negative stars I would.

Jun 01, 2020

topics are poorly explained and confusing

Jul 19, 2020

I was somewhat put off by critical comments about the third course in this series, but have to disagree with the reviewers. Yes, it is tougher and, yes, the instructor doesn't have the "schwung" of the other two instructors, but that doesn't affect the quality of this course. His walkthrough of the derivation of PCA is thorough and systematic, and builds on material that has been presented in the earlier lectures.

In fact, looking back on the entire specialisation, I would retrospectively grade the first two courses a notch lower (even if they're excellent), simply because they "sailed through" a bit too easily. The exercises in those courses required little thinking apart from recalling what was said in the lectures. In this course, exercises tended to go beyond or ahead of the material presented in the lectures. Solving them required active thinking, reading, and problem solving, which in the end brings a more thorough understanding.

Feb 21, 2020

The coverage of PCA provided by the instructor was wide and provided me with an intuitive basis for executing the PCA algorithm in the wild. Ultimately, the subject and its various steps were easy to understand. That said, I gained many great insights watching Khan Academy videos especially ones on eigenvalues/eigenvectors. By far the hardest part of the class was implementing and executing the python code. There the devil was in, and sometimes, outside of the details. I cursed the name of the Instructor more than once (a lot more). But, in the end, because of the real life, no safety net experience, I was able to jump right into PCA (and other feature engineering projects) adding value to my team at work on day 1.

Jan 20, 2019

Best course out of the series so far. A fine balance between theory and derivations, and practice with the programming assignments. It seems that they have solved their programming assignment issues (the first one still has some problems with the grader I believe). This course does require you to have some prior experience, though, so if you are new to programming or linear algebra (not just the concepts but how to apply them) it's bets to take the first two courses with some additional help (maybe Khan academy or even MIT OCW. I will certainly refer to this course in the future, as well as the professor's book on Mathematics for ML.

Sep 04, 2020

Even though one might read quite a few negative reviews about this course, I having completed this course certainly can tell that I learnt the most while doing this course. The course was indeed hard and challenging but the good thing that came out of this course was it gave me the ability to learn to study quite a few topics extensively on my own. The course had the book on "Mathematics for Machine Learning" which acted as a great supplement to this course. Overall, I'd ask anyone who is seriously interested in learning the extensive Math behind Machine learning, to take this course.