Вернуться к Mathematics for Machine Learning: PCA

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Оценки: 2,680

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Рецензии: 673

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....

WS

6 июля 2021 г.

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.

JS

16 июля 2018 г.

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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автор: Galina F

•8 янв. 2020 г.

Mathematical concepts are clear, but no explanation how to apply them to python to solve machine learning ussies. But you need this for python assignments.

Scripts checking assignments work uncorrectly such a way that one can submit the same piece of working(!) code and get 0/10 and then submit the same code and get 10/10.

All in all, it's very annoying and disappointing.

автор: Matt C

•1 июля 2018 г.

I was expecting to learn a lot in this course. I did not. The lectures don't really explain much at all and then you're thrown a quizzes and assignments that do not match what was in the actual lectures. The rest of the specialization was great but this course falls of the other two.The explanations in the videos are very poor. Really disappointed.

автор: Cy L

•9 июня 2018 г.

The course is mathematics for Machine Learning. Yet, they require that you are proficient in python. I understand the mathematics. However, no one will answer my questions on the python we are suppose to code. I passed both of the previous courses. I've taken and passed Statistics with python on edX. I've very disappointed in this course.

автор: Mojtaba B

•1 апр. 2021 г.

This is the worst course on in this specialization. The instructor is like a robot reading a text book. The material is not well constructed. It's just a bunch of formula after formula, with no intuition. It has a lot of readings, which is annoying in an online course. The programming assignments are challenging, but I found them useful.

автор: Kannan S

•11 апр. 2018 г.

There are no numerical examples as the course progresses. The instructor does everything algebraically. As a result I was not able appreciate the practical use of PCA. Later on I saw there are very nice videos in Youtube that illustrate the concept more lucidly using numerical examples. I am disappointed.

автор: John Z

•13 окт. 2019 г.

Marc Peter Deisenroth jumps too much at the important computation steps. Some steps might be simple to him, but it could be very misleading to students.

Often times, he will just throw out some equations without letting the student know what exactly we are trying to achieve.

автор: Rob E

•11 авг. 2020 г.

Intentionally obtuse. No effort whatsoever is given to helping people learn. The instructors don't answer questions and they admittedly make their lectures hard to understand.

I only took this because there were no other courses on available at the time.

автор: Shubhayu D

•13 июня 2020 г.

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.

автор: Abhishek S

•7 июня 2020 г.

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.

автор: Nathaniel F

•14 мар. 2021 г.

I think there are broken graded assessment in week 4 'test_normalization'

автор: Gita A S

•12 мар. 2021 г.

So many bugs on the programming assignment!

автор: Anton K

•14 нояб. 2020 г.

By far, this is the best out of 3 courses in this specialization. It is hard though and in the weeks 3 and 4 I had to pause and rewind almost every 10 seconds of the videos and search some error in code labs on the web. But in the end this course showed me in great detail the process of PCA and I also learned a bit of linear algebra alongside it. Considering problems with this course, there were some points that got me a little bit dissapointed. I still don't get it why are we using the biased version of variance, sometimes the notation changed a little bit, (which is not a big problem but introduces some inconvience if the material is completely new to the learner), some of the math concepts were not covered in the "linear algebra" course. But the worst problem was a technical one: some parts of the labs that are not necessary for grading but are very important for learning were throwing errors. I hope that in the future versions they will be resolved.

автор: Marco v Z

•19 июля 2020 г.

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.

автор: Ivy W

•3 апр. 2021 г.

I find this course a good use of my time, I have learnt a number of new things from it and it was quite a fun playing around with the programming assignments. What I especially like are the detailed math explanations/derivations and the reading materials/lecture notes provided (so that I have texts to refer to, instead of always having to view the videos again).

This course is obviously more challenging than the first two in the specialization (I'd say the first two are too easy as 'math' courses), one needs a good understanding of the first two, esp. linear algebra, to know what's going on here. I'm most satisfied with this course among the three, and it's sad to see so many people giving negative reviews on this and complaining on the depth of the contents.

автор: Fredrick A

•20 февр. 2020 г.

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.

автор: Abdu M

•20 янв. 2019 г.

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.

автор: ANAMITRA S

•4 сент. 2020 г.

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.

автор: Laszlo C

•6 дек. 2019 г.

This is an excellent course first covers statistics, looks back to inner products and projections, thereafter it connects all of that and introduces PCA. The knowledge that you've gathered throughout the first two courses gets applied here. Granted, it's more abstract and challenging than the others, I wouldn't give a worse rating just because of that. You'll need to dive into certain topics on your own and if you strengthen your coding skills for the programming exercises. Nevertheless, it's just as highly rewarding as the first two.

автор: Mohit

•28 дек. 2020 г.

This course is too good ,difficult level of this course from other too of this specialization is more.

Having patience and more practice lead to more successful .

If anyone want to learn Machine learning course then after doing this Machine Learning course is simple because most of the thing you have learn through this course

This specialization makes you better and better and you learn many more new and interesting thing related to real world example with practice assignment

Thanks a lot for this to all the mentors

автор: Douglas W

•22 мая 2020 г.

This was the most challenging of the three classes in the series. I thought the instructor did an excellent job of moving from theory to practice, and in the end I came away with a good understanding of the topic. As a developer, part of my personal learning style is to shadow these types of lectures in code. I did (or attempted) naive implementations on most slides - that definitely helped my comprehension of this challenging material. Be prepared to work hard, occasionally head scratch and you'll do fine.

автор: Muhammad Y A

•5 нояб. 2020 г.

Big thanks to the teacher, this is the most challenging course among the other courses on this specialization. It took me a full 24 hours to complete the final assignment, PCA Algorithm. But, it's worth it, I really enjoyed this course besides how hard it is lmao. One more, unfortunately, there will not be much discussion on the forums, since there's a few people enroll in this course compared to others and the assignment, especially the last one was very hard, anyway hope you enjoy this course, see ya.

автор: Jitesh J T

•23 дек. 2019 г.

Hi,

The course tries to cover most of the important mathematical concepts in Mathematics applied to PCA. The assignments were a bit tough, but i guess that the road ahead when we do programming for data sets in real world applications would not be that easy. Loved the way the lectures were delivered and the programming assignments help to build a strong base for applications of linear algebra that we have done earlier.

Thanks and Regards

Jitesh Tripathi, PhD in Applied Mathematics

автор: Taranpreet s

•26 сент. 2020 г.

Although there are glitches with the submission of assignments and some of the videos by instructor are brief, I will still rate it as a 5 star for the content covered. This is 3rd course of the specialization and need solid understanding of the concepts covered in first 2. Considering the more challenging content covered in this course, Instructor did a great job. All instructors in the specialization are awesome, Would love to do more advanced courses from the same team.

автор: Tarek L

•11 сент. 2019 г.

This is a difficult course, but it really gave me an appreciation of the mathematics behind machine learning. I encourage anyone doing this course to read Deisenroth's free book Mathematics for Machine Learning (mml-book.com) to better understand the notation and technique used to get to the proofs. If anything, the rigor of this course inspired me to further pursue learning in mathematics to strengthen my machine learning foundation.

автор: ChristopherKing

•18 апр. 2018 г.

The whole content of this course is fantastic, not all details were covered in the video, but main ideas were expressed in a great way buy math formulations. Pay attention to those vectors and matrices, especially their dimensions, this will help you solve problem quickly. More important, matrix is just a way to express a bunch of similar things, knowing the meaning of those basis vectors is important.

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