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Ask HN: Top Coursera Courses?

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Re: Ask HN: Top Coursera Courses?

#71

Post by ayewo »

joshlk wrote: post #23
commonturtle wrote: post #3

Scott Page's Model Thinking course: https://www.coursera.org/learn/model-thinking . Broad overview of how to think in models to understand the world around you.

What does that mean to think in models?

Models are tools to help us reason clearly about the world. They help us deal with complexity in manageable chunks by focusing on salient parts of a problem, rather than the whole.

From my course notes, a nearly comprehensive summary of how the social sciences use models was presented:

16 uses of modeling outside prediction include from Epstein, Joshua M. (2008). Why Model?. Journal of Artificial Societies and Social Simulation:

1. Explain (very distinct from predict)

2. Guide data collection

3. Illuminate core dynamics

4. Suggest dynamical analogies

5. Discover new questions

6. Promote a scientific habit of mind

7. Bound (bracket) outcomes to plausible ranges

8. Illuminate core uncertainties

9. Offer crisis options in near-real time

10. Demonstrate tradeoffs / suggest efficiencies

11. Challenge the robustness of prevailing theory through perturbations

12. Expose prevailing wisdom as incompatible with available data

13. Train practitioners

14. Discipline the policy dialogue

15. Educate the general public

16. Reveal the apparently simple (complex) to be complex (simple)

Re: Ask HN: Top Coursera Courses?

#72

Post by st1x7 »

yawz wrote: post #15

Dr Barbara Oakley’s “Learning How to Learn” [0] is great. Edit: It gives an important understanding on how our minds function and how we learn, which, I think, forms the basis of effective work. Knowing how to work, and being an effective learner are incredibly important qualities in life. [0] https://www.coursera.org/learn/learning-how-to-learn

I would recommend skipping this one. It contains fairly obvious advice, the information density is low and it isn't delivered very well. I really don't know why it's recommended so often.

Re: Ask HN: Top Coursera Courses?

#73

Post by passwordreset »

arcturus17 wrote: post #19

Princeton's and Stanford's Algorithms are fantastic.

I was disillusioned by Princeton's Algorithms course when I took it. I felt like I was fighting too hard with eccentricities of Java than actually learning about algorithms. In retrospect, I was wrong, because one of the things I realized that it taught me was that implementation details of algorithms are _hugely_ important, and can cause drastic differences in time and memory utilization. I can easily forget the details, but I haven't forgotten the experience of hacking out my stupid mistakes until the implementations fit the requirements.

Re: Ask HN: Top Coursera Courses?

#74

Post by mlinksva »

veganjay wrote: post #43

I can think of many - I have taken several starting since 2013. The tricky thing is that Coursera classes seem to get merged, re-mashed or otherwise re-branded. And as such only one currently is listed in my "Completed" courses section of my profile. Having said that and with the caveat that these probably changed since I taken them, I recommend the following: - Cryptography - https://www.coursera.org/learn/crypto -…

> as such only one currently is listed in my "Completed" courses section of my profile.

That's surprising to me: wouldn't Coursera want learners to be reassured that whatever signalling benefit there is to completing a course will remain forever?

I took a few courses in 2013 just to see what MOOCs are really like and completed two (Programming Languages, as taken by many here, and Introduction to Mathematical Thinking, which IIRC was mostly about logic) which indeed are not listed under "completed" in my profile. I found them at https://www.coursera.org/accomplishments though.

Re: Ask HN: Top Coursera Courses?

#75

Post by budoso »

shuckles wrote: post #66
BoiledCabbage wrote: post #63

Earlier quoted context omitted.

I could be wrong, but I think it has nothing to do with that, and now to do with the course being so old that R and Python weren't the standard ML languages yet.

You are right. Recent offerings of the course are in Python: cs229.Stanford.edu.

I still think that for learning the math behind ml Matlab still makes the most sense though. It takes the focus off the programming itself and enforces the matrix concepts. Although python is the undisputed king in that regard so unfortunately it makes more sense to teach that

Re: Ask HN: Top Coursera Courses?

#76

Post by arcturus17 »

passwordreset wrote: post #73
arcturus17 wrote: post #19

Princeton's and Stanford's Algorithms are fantastic.

I was disillusioned by Princeton's Algorithms course when I took it. I felt like I was fighting too hard with eccentricities of Java than actually learning about algorithms. In retrospect, I was wrong, because one of the things I realized that it taught me was that implementation details of algorithms are _hugely_ important, and can cause drastic differences in time and memory utilization. I can easily forget the det…

That was a learning for me too, but I thoroughly enjoyed the psets. I don't mind Java - setting everything up can be annoying but I find it fairly straightforward and its massive standard library comes in handy for a class like this.

Re: Ask HN: Top Coursera Courses?

#77

Post by xenocyon »

shuckles wrote: post #66
BoiledCabbage wrote: post #63

Earlier quoted context omitted.

I could be wrong, but I think it has nothing to do with that, and now to do with the course being so old that R and Python weren't the standard ML languages yet.

You are right. Recent offerings of the course are in Python: cs229.Stanford.edu.

That's not the entire explanation for Ng's use of Octave though.

At the birth of Coursera in 2012, R and Python were already clearly established in the field of data science. R was the dominant open-source language for data science, with Python very close behind (and already gaining ascendancy among folks who identified with "machine learning" rather than "data science"). I remember Matlab/Octave being more associated with academics/students in EE (signal processing, wireless communications, and the like); if you want clear insight into matrix operations, Octave is great.

I think Ng made a very conscious decision at the time to eschew built-in functions and not get distracted by trendy languages - hence the use of Octave to learn how to implement algorithms at the most basic linear-algebra level.

Even at the time his decision was not well understood nor popular - way back then I remember people asking "Why Octave instead of R or Python?"

Re: Ask HN: Top Coursera Courses?

#80

Post by qorrect »

Nowado wrote: post #41

I have no idea how 'Machine Learning' from Ng is not mentioned. It's fine in teaching you introductory (although it seems to cover more basics than a lot of other courses do, somehow) ML. But more importantly, it's a well designed course. You can see how each piece uses previous pieces and how it solves problems and edge cases not covered earlier.

I can still hear him saying multivariate. Amazing course.
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