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# Coding the Matrix: Linear Algebra through Computer Science Applications

16 Reviews 455 students interested
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## Overview

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When you take a digital photo with your phone or transform the image in Photoshop, when you play a video game or watch a movie with digital effects, when you do a web search or make a phone call, you are using technologies that build upon linear algebra.  Linear algebra provides concepts that are crucial to many areas of computer science, including graphics, image processing, cryptography, machine learning, computer vision, optimization, graph algorithms, quantum computation, computational biology, information retrieval and web search. Linear algebra in turn is built on two basic elements, the matrix and the vector.

In this class, you will learn the concepts and methods of  linear algebra, and how to use them to think about problems arising in computer science.  You will write small programs in the programming language Python to  implement basic matrix and vector functionality and algorithms, and use these to process real-world data to achieve such tasks as: two-dimensional graphics transformations, face morphing, face detection, image transformations such as blurring and edge detection, image perspective removal, classification of tumors as malignant or  benign, integer factorization, error-correcting codes, and secret-sharing.

## Syllabus

• The Function
• The Field
• The Vector
• The Vector Space
• The Matrix
• The Basis
• Dimension
• Gaussian Elimination
• The Inner Product
• Orthogonalization

Phil Klein

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## Reviews for Coursera's Coding the Matrix: Linear Algebra through Computer Science Applications 3.6 Based on 16 reviews

• 5 stars 44%
• 4 stars 19%
• 3 stars 13%
• 2 star 6%
• 1 stars 19%

Did you take this course? Share your experience with other students.

• 1
Prose S
4.0 4 years ago
by completed this course, spending 9 hours a week on it and found the course difficulty to be hard.
Now finished, I remain torn about this course. But I've bumped it up to four stars.

Positives:

- using Python

- having to use doctests (yes, seriously, I didn't really understand the funny comments preceded by >>> before this :s)

- building my own sparse simple matrix and vector classes

- GF2 ( = "binary arithmetic without the carry digit" when this hayseed finally figured it out)

- carefully crafted material and lectures

- linking lin alg concepts to applications (my faves: perspective correction, and …
1 person found
this review helpful
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Mark W
3.0 5 years ago
completed this course.
This is another course I felt torn about. On the good side, the idea is fantastic! Why not use programmers’ systemic thinking abilities as a springboard to learn linear algebra more quickly? When I studied linear algebra long ago, it used quite a few examples from calculus and electricity and magnetism. While that was a good approach, I feel that linear algebra is a subject that is worth studying for more people than multivariate calculus is. That alone made me optimistic about the course. Unfortunately, the automated grader was horribly buggy and so much of a pain to deal with that I decided to use my study time on other classes.
6 people found
this review helpful
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Vlad P
5.0 4 years ago
by completed this course, spending 12 hours a week on it and found the course difficulty to be medium.
I found this to be a great preparatory course for "practical" topics in data science, such as linear models and machine learning. The homeworks and labs are well thought out, interesting, and very valuable for a good understanding of the material. There is also the bonus of getting to learn a bit of python (very popular programming language in data science) if you're not already familiar with the language.

To respond to older reviews of this course, the matters must have improved considerably with respect to submitting and grading the homeworks and labs. I didn't experienced an…
2 people found
this review helpful
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Anonymous
1.0 6 years ago
completed this course.
Simply stated, this course was a disappointment. I initially had high hopes, based on the description. However, I found a few aspects disturbing. The first was the use of a relatively recent feature of the latest version of Python. The second was that the optional text was not offered until well into the course. The third was that the lectures did not seem to match the quiz material. The fourth was that several of the student-posted forum questions went unanswered even through dozens of requests for clarification.

My technical background should have been sufficient, based on the course description, but I withdrew from the class in frustration. I note that I have been pleased with several other online technical classes, and some are even rated in this forum.

I will not attempt another offering from this instructor.
4 people found
this review helpful
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Anonymous
5.0 6 years ago
completed this course.
For me this has been the first course I've taken in coursera and it wasn't a disappointment at all. The lectures, although a bit hard to understand at the beginning, become clearer when you get to the assignments. The instructor was great at the explanations, and the forum was a very good resource as well. I have strong programming background in languages other than python, but it's a really easy language to work with and we didn't get to any advance feature.

I'll hope for new offering from this instructor, and hopefully a continuation of the topics from this course, like linear programming.

I hope there will be new offering from this instructor, and hopefully a continuation of the topics from this course, like linear programming.
2 people found
this review helpful
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Anonymous
4.0 6 years ago
completed this course.
I liked very much, but some problems to submit the exercises ...I wasted a lot of time to find where were the problems... you need to submit the exercise in the order they appear...it took me a long time to figured out ...
2 people found
this review helpful
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Anonymous
2.0 4 years ago
is taking this course right now.
Unfortunately the course is much more about learning the specifics of Python 3 than about Linear Algebra. As I progressed through the course, I found I was learning a little bit about Python's capabilities and a very little about Linear Algebra. I decided to use a book instead.
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Anonymous
5.0 4 years ago
completed this course.
This is a great course.The Professor interacts with the students and the course materials are exhaustive. Programming assignments are both challenging and rewarding and requires due diligence on the student's part.
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Anonymous
5.0 a year ago
completed this course.
One of the best MOOC I have ever take. A rigorous and innovative introduction to linear algebra with very interesting examples and applications.

Professor Klein make clear and simple every concept.
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Benjamin K
1.0 a year ago
by is taking this course right now.
Poor quality would not recoment using this, look at ex trevtur on youtube or anything else as this is a bad source.
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Michael A
5.0 4 years ago
by completed this course.
1 person found
this review helpful
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Mark B
5.0 3 years ago
by completed this course.
1 person found
this review helpful
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Ajay M
5.0 4 years ago
by completed this course, spending 4 hours a week on it and found the course difficulty to be hard.
1 person found
this review helpful
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Mihailo M
3.0 5 years ago
is taking this course right now.
0 person found
this review helpful
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Colin K
4.0 3 years ago
by completed this course.
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Anonymous
1.0 3 years ago
is taking this course right now.
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• 1

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