임페리얼 칼리지 런던의 Mathematics for Machine Learning: Linear Algebra 학습자 리뷰 및 피드백

Your discussion forum really needs to be better. It seems to be the only place where any sort of doubt clearing can be done and very rarely have I seen TA's answering unless it's a grading issue. The problem with this sort of answering is that if any coding concepts are unclear, either they are solved by trial and error or after going through Google multiple times. And for a course that is paid for, I shouldn't have to make this much of an effort just to have my doubts cleared.

The course starts well and in general the first instructor does a good job trying to help the student develop an intuition of the concepts. However, weeks 4 and 5 are extremely weak. Very important concepts like eigenvalues and eigenvectors are poorly explained. The final quiz on these concepts asks questions that were never discussed or explained in the videos. I found I needed to go elsewhere on the Internet (like 3Brown1Blue) just to help me get through some of the quizes.

Teaching quality is good overall, except for a few jumps towards the end, where it's hard to follow. Quizzes and assignments well designed.

Unfortunately, and contrary to other courses I've taken, the forum seems completely un-monitored (as of May 2019), so don't expect much help from there.

Overall it's a good start of the specialization. Sadly, the teaching quality of the other two courses (multivariate calculus and PCA) is way below the standards of this one.

Without about intermediate knowledge on vectors, you would be having a tad difficult time doing this course. I have already studied Linear Algebra in my University, attaining an A grade and found the explanation for some of the parts a bit confusing. It is not bad by any means, but just that I believe a more structured/concise explanation for some of the terms might be more accessible for beginners or students who have never learned about Lin Alg before.

The course was informative on most of the Linear Algebra you need for machine learning. The programming assignments and quizzes are mostly relevant to what you learn beforehand. However, some of the videos feel rushed and I found it difficult to take notes in time without having to replay the videos. There is also a lack of written content after each video describing the content covered, which would have been a godsend for me with my method of learning.

Definitely enjoyed some parts of the course but in general, the explanations are brief, requires spending significant time outside of videos on Youtube, discussion boards etc as they skip or miss key points for a beginner to grasp Linear Algebra concepts. Happy that I completed the course, but a lot of improvements should be made by including course notes that supplement common queries/misunderstandings students have in relation to the course materials.

This course is not very hard for students with some maths foundations like me, but the programming assignment is too hard, even though I knew some basic Python knowledge. Two lecturers sometimes could not explain one concept clearly with some typical examples. I could only learn by doing assignments or use formulas to calculate without real understanding. This course is only for some basic concepts but not solid learning.

The videos were well structured, but the quiz sometimes were far more difficult than the practice questions in video. I had posted on forum but no comment nor reply. Quiz answers were not elaborate enough to understand after making mistakes. So I had to ask my father who's extremely good at maths many times, for explanations. Without hi help I might not have been able to understand as well. Need improvement.

This class fluctuates between impossibly hard, because a lack of instruction and examples were provided and too simple, because the same question is asked repeatedly. There is also very little mention of machine learning, despite the name of the specialization/course. That said, it was still the best survey course of the linear algebra concepts most relevant to machine learning.

The course starts off well enough, but by week 4 the intuition for certain concepts is not imparted well at all, and the correspondence between notation from the lectures and that used in the practice quizzes breaks down badly.

I gave it 3 stars instead of 2 stars because the geometric intuition that is imparted is quite good, even though at times the notation is sloppy!

Having no background in linear Algebra made it difficult to complete the quizzes, assignments and exams. Even with the instruction (which was good) I found the hands on portions to be different from what was being explained in the videos. I will instead have to take the key concepts and do more research on my own to fully understand them.

Like most of Coursera's courses there are no staff members available in the forums (which is extremely shameful for Coursera - repeating the same boring pattern over the years). Don't even try it if you have never seen linear algebra or python before. Otherwise, it's useful for practicing a few concepts or refreshing others.

Nice course, with many insights. Sometimes the topics are given too quickly, I would have rather preferred less arguments but discussed more thoroughly. Nevertheless, I think this is a good one, especially if you've already got some background and you're looking for some general content to build upon it using academic books.

Overall, I learned some new stuff in this course. The programming exercises were interesting. However, the instructors really should provide more explanations regarding the calculations, formulas, and exercises' instructions and feedbacks. In many occasions, I had to looked up explanations online to solve the homework.

Very challenging and interesting. However, the last module was a bit confussing and needed to look for materials on the Internet to really grasp a bit of understanding on the subject. Although sometimes frustrating, I think it is a good start to recap mathematics with a very practical approach.

Overall, it was a good summary to understand linear algebra. To get into the topic, I had to read through additional material as the videos and tasks provided in this course were a little shallow to my liking. I, personally would have liked more applicable machine learning examples.

I believe that the programming required in the assignments are not beginner level. I had never coded on Python before and I thought that there wasn't enough support on how to test my code before submitting, for example. On the other hand, the math topics were really interesting.

I give this a three because the course focuses on themes with a macro lens while not giving the microdetails much explanation. Good foundation and interesting topic, but it seems counterintuitive for me to have to supplement the lectures with youtube lectures...

It was too fast for me. I answered many questions just by chance. But i got an overview of the concepts like diagonalisation , inverse, transpose, basis, span , eigen and so on. I am hoping that i will build on this.

I am feeling like something is missing during the last part of the course when it comes to Page Rank Algorithm. There should be more explanation to how the math works or comes to its formula.

The assignments kill this course, great instructors, and pace, in my opinion. (I am a beginner in linear algebra and I understood the concepts without needing Google or external resources)

I think the course is more suitable for those who have had comprehensive theoretical knowledge in linear algebra and intend to learn more about its practical use and its relevance to code.

The course is good. But the last module (week) is not so good. I think that the explanation of the Page Rank algorithm is not very good. I also think that the final test is very confuse.

Overall course was good, I have learnt few new concepts which I haven't know till now. But at the end, things were not clear while putting all together for solving page rank algorithm.