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Hacker News Comments on
Introduction to Graph Theory

Coursera · University of California San Diego · 1 HN comments

HN Academy has aggregated all Hacker News stories and comments that mention Coursera's "Introduction to Graph Theory" from University of California San Diego.
Course Description

We invite you to a fascinating journey into Graph Theory — an area which connects the elegance of painting and the rigor of mathematics; is simple, but not unsophisticated. Graph Theory gives us, both an easy way to pictorially represent many major mathematical results, and insights into the deep theories behind them.

In this course, among other intriguing applications, we will see how GPS systems find shortest routes, how engineers design integrated circuits, how biologists assemble genomes, why a political map can always be colored using a few colors. We will study Ramsey Theory which proves that in a large system, complete disorder is impossible!

By the end of the course, we will implement an algorithm which finds an optimal assignment of students to schools. This algorithm, developed by David Gale and Lloyd S. Shapley, was later recognized by the conferral of Nobel Prize in Economics.

As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.

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Provider Info
This course is offered by University of California San Diego on the Coursera platform.
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See also: all Reddit discussions that mention this course at reddsera.com.

Hacker News Stories and Comments

All the comments and stories posted to Hacker News that reference this url.
So I actually went ahead and decided to build "curriculum" that I would be happy to study, instead of trying to take potshots at the idea. For reference, I'm a to-graduate-undergrad who's studied a pretty theory CS-heavy course curriculum. I work [in terms of research] in compilers, formal verification, and dabble with some NLP on the side. I personally find knowing pure math, theory CS, and algorithms/data structures (the ones that are derided often here on HN as "leetcode") to be an _insane_ force multiplier.

If I had to recommend online courses, here are the ones I would recommend. Unfortunately, one does not get access to exercises and folks who are willing to verify your work. Math.stackexchange is unfortunately far more active than cstheory.stackexchange. I don't really know of an effective way to "bootstrap" this, except for implementing a lot of the things that show up in computer science.

I'm collecting links of courses that have videos, lecture notes, and exercises, which I would be happy to learn from [or have learnt from in the past].

Theory courses that are must-know:

- Linear algebra: https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra...

- Basic Combinatorics: https://www.coursera.org/learn/combinatorics#syllabus

- Introduction to Algorithms by Erik Demaine: http://courses.csail.mit.edu/6.006/fall11/

- OR, Introduction to Algorithms by Robert Sedgewick: https://www.extension.harvard.edu/open-learning-initiative/a...

- Complexity theory/theory of computation: https://web.cs.ucdavis.edu/~rogaway/classes/120/spring14/

- Structure and interpretation of computer programs: https://ocw.mit.edu/courses/electrical-engineering-and-compu...

Computer engineering courses that are must-know: I do not immediate know of good online courses, so I list the topics below

- Operating systems:

- Networks

- Computer graphics [Is a great applied course to see linear algebra in action]

- Distributed systems

- Compilers

- """Machine learning""": Scarce quotes since there's a divide between old-school machine learning and newfangled deep learning. Is useful to know ideas from both.

Advanced good-to-haves:

- Advanced Data structures: http://courses.csail.mit.edu/6.851/fall17/

- Graph theory: https://www.coursera.org/learn/graphs#syllabus

- Abstract Algebra: https://www.extension.harvard.edu/open-learning-initiative/a...

- Nand2Tetris, where one builds a computer "from scratch": https://www.nand2tetris.org/software

- As much math, physics, and computer science as can be learnt!

emmelaich
Is Linear Algebra that useful to the typical working programmer? Or is it perhaps only particularly useful for machine learning and similar?

To me, combinatorics, probability and statistics are much more used day to day.

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