Most fun: Pat Pattison, Songwriting, Coursera. Very good lectures, very good material, very well presented. Teaches a lot about writing song lyrics in just 6 weeks, breaks it nicely down to steps and recipes. I used to think that the best feature of MOOCs is the automatic grading and feedback from programming homework, but in this course, for the homework songwriting you gave and got feedback from 3-5 random people in the course, and it was not only useful but this feeling of togetherness with strangers was even better than getting instantaneous feedback from a bot for programming homework. Shows that teaching art scales to MOOCs as well.
Nicest: Andrew Ng, Machine Learning, Coursera. Interesting topic, well-planned material, very well avoids going into the mathy details, but still conveys a feeling of understanding of the topic, so accessible to a wide audience. (Martin Odersky, Functional Programming Principles in Scala, Coursera, was almost equally nice, but had some rough edges in the first run.)
Most interesting: Probabilistic Graphical Models, Daphne Koller, Coursera. Very interesting topic. I took the first run of the course and it had lots of rough edges. Needs a lot of work to apply the lectures to the homework. I haven't seen such a demanding course since I took quantum mechanics at university.
Best organized: Jennifer Widom, Databases, Stanford. This is not the flashiest of a topic, but oh boy was it well organized. Runs like a clockwork. Everything in the lectures is relevant, everything from the lectures is applied and tested in the homework, there is lots of homework (but still not enough to make you remember SQL,XPath,XQuery,XSLT for the rest of your life if you don't keep using them), weekly homework has a nice progression from simpler things to medium difficult things, and the web environment is well designed, and gives wonderful feedback and guides you to get your queries correct.
> Nicest: Andrew Ng, Machine Learning, Coursera. Interesting topic, well-planned material, very well avoids going into the mathy details, but still conveys a feeling of understanding of the topic, so accessible to a wide audience. (Martin Odersky, Functional Programming Principles in Scala, Coursera, was almost equally nice, but had some rough edges in the first run.)
Have to agree with all of this. I've taken Andrew Ng's Machine Learning course (only time I've paid for a 'verified' certificate), and found it a great overview of ML, though I'm not sure I'd feel comfortable telling anyone I have a good understanding of ML :)
Odersky's FP in Scala was actually the first Coursera course I took (during its initial run, I think). -- I also found the follow up Reactive Programming course to be excellent as well.
> Jennifer Widom, Databases, Stanford. This is not the flashiest of a topic, but oh boy was it well organized.
Couldn't agree more. I took this course in 2011 but didn't have a need for working with databases until 2014. Three years after I took the course I was able to jump in and work fluently on databases -- Mongo, Sqlite, Firebase, etc. The least I can say is the course helped me internalize database concepts.
This course has the right amount of handholding yet challenges you enough so you acquire long-lasting skills.
This thread's full of really valuable information, so I've compiled these and a bunch of the other courses mentioned here into a big document[1]. I've also added quotes from this thread, with links back to the original comments.
Andrew Ng's class was great! For a tougher class that focuses on one of the technologies, I recommend Geoffrey Hinton's "Neural Networks for Machine Learning" on Coursera. Really eye opening for me, and fairly close to the leading edge in deep learning, as far as I can tell. I felt that the exercises were more detailed and challenging than in Ng's class, and thus I ended up learning more.
– the first course in the specialization has a very good and engaging start, but the gap between lectures and problems widens quickly after that (maybe that's why the author boasts about a "challenging" course "not for everyone"). I'm hesitant to take the next course of the specialization.
It's good to hear that this MOOC is still so well thought of since I first took it; for me, it was the first course I took that made me really understand how a neural network and back prop actually worked.
When I took the course, it was in 2011 - and was known as "ML Class"; yep - I was among the first "beta tester guinea pigs" of the course. It was fun and amazing to participate in.
One of the early participants was even inspired to replicate CMU's ALVINN self-driving vehicle in miniature:
Pat Pattison, Songwriting, Coursera! I took this too! Fantastic! Delightful to learn something (anything!) from someone who so thoroughly and completely knows just what he has to say to teach a topic he's expert at.
Yes, I enjoyed the Pattison songwriting class as well. At first his applying the "strong" and "weak" concepts to every aspect of a song lyric seems overly simplistic, but it actually starts to make sense. I came into class with considerably previous experience in writing poetry, but thought I learned a lot from him and my peers. His breaking down a performance coaching example was also very instructive.
Other music MOOCs I enjoyed:
The Berklee "Developing your Musicianship" series on Coursera taught by George W. Russell. Started off thinking this was too elementary, but the ear training is valuable, and I learned a lot about the use of diatonic chords, and even the few simple patterns he taught improved my song writing enormously.
The Berklee "Jazz Improvisation" class taught by Gary Burton. Very cool to be taught by a living legend, and his selection of songs was refreshingly modern. On the down side, skill levels of the students varied widely, so peer review was more miss than hit.
On the Pattison one, out of curiosity did it use his Writing Better Lyrics for the material? I've read the book and love it to pieces even though I write prose, the way he explores words and phrases is sort of magical.
I wanted to invest better so I took this course to learn the basics of financial markets (I'm a software guy and have zero training in finance). After taking it, not only do I have the basics nailed down but have gained a massive appreciation of finance as a technology that, at its best, mitigates risk and advances society.
Shiller is an authority on the topic, having won a Nobel Prize in Economics no less. His penchant for financial market history and human behavior angle on things is a massive plus for this course. I'd say the course is useful education for entrepreneurs and curious folks alike.
It's kind of crazy that, at least in the US, personal finance is not taught so much in grade school. I had to do a lot of reading and research now in my late twenties to figure out the best way to manage the RSUs I get at work and how to plan for a home purchase and retirement. It turns out (surprise) that most financial institutions don't have regular investors' best interest in mind. Instead, they see us as customers with value to siphon out over long periods of time.
Understanding how economies work[1], how financial service companies sell products, theories behind volatility and market forces, and how simple portfolio management can be goes a long way to improving an individuals ability to efficiently self-manage their finances.
I actually just finished this course and loved it! It covered all the financial ground that I was looking for... stock, options, brokers, financial planning, insurance, and financial theory.
As a serial MOOCist I cannot single out any one so here is a list per domain.
Data Science
Introduction to Probability - The Science of Uncertainty,math oriented MIT/EDX
Difficulty:5/5 Videos:5/5 Material and exercises:5/5 Usefulness: 5/5
Learning from Data, math oriented formerly Caltech/EDX now on caltech, check the exercises and you will see the difference in quality with Andrew Ng:
Difficulty:4/5 Videos:4:5 Material and exercises:5:5 Usefulness:3/5
The Analytics Edge - Bertsimas MIT/EDX.You will learn practical stuff in R includes a kaggle competition.
Difficulty:3/5 Videos:4/5 Material and exercises:6/5 Usefulness:6/5
Computational Probability and Inference MIT/EDX Computational probabilty using python.
Difficulty:2/5 Videos:3/5 Material and exercises:6/5 Usefulness:5/5
Basic Modeling for Discrete Optimization: Uses an easy to learn language called minizinc which has multiple backends and is useful for those types of problems. VERY pleasant to watch videos.
Difficulty:2/5 Videos:4/5 Material and exercises:3/5 Usefulness:5/5
Deep learning: deeplearning.ai coursera and fast.ai for more practical stuff.
Non data science:
I have not done the exercises on these just watched them:
Learning how to learn: Life changing I wish it existed many years ago.
Influencing People: Puts things into perspective. Makes you ponder about morality
Roman Architecture: Includes the "why" it is like the old "who moved my cheese" book, but in roman architecture edition.
Explaining European Paintings, 1400 to 1800: What it says on the tin.
Economics of money and Banking: In all tuthe courses I have listed the professors are very good. But this guy.... Makes a difficult subject so approachable and watching the news becomes as painful as watching a train full of passengers going to broken bridge
I am sure I have forgotten others
MOOCs have changed my life, financially and in other ways. I thank all the people involved.
Nand2Tetris was my favorite. I can't say the information (particularly the first half) has much practical application for me, but it was a lot of fun and deepened my understanding of what's going on at a low level. Homework is very well designed with a simulator you download to test your work on, then submit for automatic grading.
Udacity's Differential Equations course was pretty awesome too. I had taken Calculus previously, but I believe it's pretty approachable even if you haven't. The homework was very well designed, and involved fun problems like computing gravitational slingshots and curing diseases.
Coursera's "The Unwritten Constitution", also has a similar "The Written Constitution". Both are pretty awesome and really gives an in depth view of what the constitution is about (spoiler alert: it's about slavery), and even points out holes that haven't been challenged yet. Homework was writing essays and grading other people's, so not that well designed in that respect.
Coursera's "Coding the Matrix" is a Linear Algebra course. I took it the first time it was offered, and you pretty much had to buy the accompanying book to follow along. And the book unfortunately had a lot of "first version" issues. A lot of the homework wasn't explained very well, but it was all auto graded code. I think the issues with the book have been addressed with the second edition, not sure about the homework. I had already taken linear algebra before, so this was mostly a refresher, but even I found it hard to follow along in the last part, and never completed the last homework assignment.
On Youtube you can find "Fundamentals of Small Arms Weapons" from 1945. It shows how the action of a small arms rifle works. It starts as just a tube with a bullet, and works up to several different types of fully automatic actions. It's just a couple hours long.
Coding The Matrix had a great concept. The learn-math-by-coding approach allows the student to see applications of the material from early on. The treatment of complex numbers was especially strong.
The tradeoff is that the student must debug during the exercises (an activity which is unrelated to the material), but it's worth it.
Regrettably, Coding the Matrix was taken down down along with a lot of first-generation Coursera courses. However, there's still the book, the website at http://codingthematrix.com and the the lectures from the Brown University version of the course:
Yes! Nand2Tetris came about before "MOOC" was even coined. It's a great course where you start building simple circuits in a hardware simulator, and eventually build a working Tetris game -- on hardware you built (running in a simulator), in an OS you wrote.
I loved both Nand2Tetris and Coding the Matrix, although I had different experiences with them. For Coding the Matrix, I didn't buy the book, but just watched the videos and worked through the course work. This was super fun, but I could see someone who was not proficient in python getting really frustrated. I took it the first time it was run as well, and there were a couple of rough areas, but overall it was really enjoyable.
With Nand2Tetris, I just bought the book and worked through it without taking a class. I'd sketch out the hardware literally on the back of napkins and then try it in the simulator when I got home. It was incredibly fun, and I loved how it took everything down to first principles.
They are consistent, not very buggy, gamified, and consumable in small or large amounts. Sal Khan is a good communicator and the videos are decent, but it's the exercises that make Khan Academy exceptional.
Khan Academy filled the gaps from my inconsistent public schooling (moved a lot as a kid). Used to think I was just dumb (I might still be lol), but turns out missing some of the early math concepts is extremely destructive to later learning. Fill the gaps and everything else becomes so much easier.
I hear tell Sal Khan is hiding out in the Bay Area somewhere, really wish I'd bump into him in a bar so I can grab his tab or something. Dude's a hero to me.
This is probably the right answer, boring as it is.
I also loved that I would actually see Sal's videos come up as the top results for my calculus questions to illustrate things like matrix multiplication.
It's not often I feel like YouTube hits are as dead on the money for me as they used to be in thsoe days.
The 10-minute limit on videos at the time was to YouTube what the 140 character-limit was to Twitter.
I have been assisting my younger brother in his math deficiencies. Khan Academy has made it so much easier. I'll have him watch several videos on the topic we need to cover, and then work through his assignment together. This lets me focus on the specific areas he didn't understand instead of trying to reteach everything. Really wish I had this when I was younger.
This is probably the right answer, boring as it is.
I also loved that I would actually see Sal's videos come up as the top results for my calculus questions to illustrate things like matrix multiplication.
It's not often I feel like YouTube hits are as dead on the money for me as they used to be in thsoe days.
Taught by Prof. Scott E. Page, teaches about models in several fields and how they're used to aid thinking about complex issues by careful design and usage.
A couple of insights: all models are wrong but some are useful. Having many models about a situation to help your thinking is better than having only one, and much better than none. Complex models are not necessarily better than simple ones.
Fantastic course, more focused on theory than programming, but full of deeply fascinating commentary on what is knowledge, intelligence, learning, etc. and what does it mean for a program to demonstrate it (ie. what is AI anyway?).
My daughter was about 18 mo. old at the time I took the class, it was an outrageously awesome added bonus to watch a little human learn all the things I was trying to get a computer to learn at the same time.
It's just fantastic. He explains what money really is from the perspective of treating everyone as a bank. Also, lots of good history here including the history of central banking, the gold standard, and war finance.
Anyone who wants to understand money should take this course. It would be nice if more cryptocurrency enthusiasts learned this kind of monetary economics.
1. Quantum Mechanics and Quantum Computation (on edX, from UC Berkeley: https://www.edx.org/course/quantum-mechanics-quantum-computa...), taught by Umesh Vazirani. Intro to quantum computing that made clear key ideas in quantum mechanics, almost in passing. The first of over 70 MOOCs I completed, not available at the moment.
2. Astrophysics (on edX from Australian National University, 4-part series: https://www.edx.org/xseries/astrophysics) taught by Brian Schmidt and Paul Francis. Delightful. Plenty of math but mostly at undergrad level. A grand tour of current topics.
3. First Nights - Handel's Messiah and Baroque Oratorio (on edX from Harvard: https://www.edx.org/course/first-nights-messiah-harvardx-mus...) taught by Thomas Forrest Kelly. Historical perspective and structure of the music. I was hooked from the first lecture. One of a series of 5 outstanding courses in the "First Nights" series, this is my favorite.
Another oddball choice for HN, but the Coursera course Think Again: How to Reason and Argue, by Duke University's Ram Neta and Walter Sinnott-Armstrong [1] is exceptional.
The subject matter covers a staggering breadth of topics, which can be characterised as either (a) fundamentals of philosophical reasoning, or (b) stuff that amateur internet-debaters think they understand but actually don't.
Other great courses: Learning to Learn, Irrational Psychology by Dan Ariely, and Algorithms by Sedgewick
Can someone recommend a good way to work with other students on MOOCs? I've taken many courses, but they aren't much better than just reading the textbook and working on a personal project, although the curation of content is valuable.
The relationship aspect is sorely missing from online courses. If there was an easy way to have a classroom setting with highly motivated peers each following the MOOC with a collaborative environment, then I would definitely want to sign up. You say that's what college is for? Well I've already graduated, signing up for random college classes is extremely expensive and the peer group is highly variable.
I found the Isbell+Littman combo to work so well that I also took the ML course. I know some people complain about their humor but it was perfect for me. I could listen to those two explain just about anything. I still LOL when I think about Littman saying to Isbell something like "are you trying to teach us something by making this lecture infinitely long?" Who knew RL could be funny?
These courses are for beginners, but I started with what I learned from a few courses in Coursera and turned it into a career as a software engineer. https://www.coursera.org/learn/learn-to-program and https://www.coursera.org/learn/program-code from Jennifer Campbell and Paul Gries from the University of Toronto laid a great foundation to build on. I think I took them the first time they offered it and I still don't understand how they completely nailed a new medium like that first try. It was very accessible, but with enough detail to make sense and the videos were so clear and concise.
The Python one from Rice University, is a fun, awesome course, where you build games to learn. https://www.coursera.org/learn/interactive-python-1
[+] [-] sampo|8 years ago|reply
Nicest: Andrew Ng, Machine Learning, Coursera. Interesting topic, well-planned material, very well avoids going into the mathy details, but still conveys a feeling of understanding of the topic, so accessible to a wide audience. (Martin Odersky, Functional Programming Principles in Scala, Coursera, was almost equally nice, but had some rough edges in the first run.)
Most interesting: Probabilistic Graphical Models, Daphne Koller, Coursera. Very interesting topic. I took the first run of the course and it had lots of rough edges. Needs a lot of work to apply the lectures to the homework. I haven't seen such a demanding course since I took quantum mechanics at university.
Best organized: Jennifer Widom, Databases, Stanford. This is not the flashiest of a topic, but oh boy was it well organized. Runs like a clockwork. Everything in the lectures is relevant, everything from the lectures is applied and tested in the homework, there is lots of homework (but still not enough to make you remember SQL,XPath,XQuery,XSLT for the rest of your life if you don't keep using them), weekly homework has a nice progression from simpler things to medium difficult things, and the web environment is well designed, and gives wonderful feedback and guides you to get your queries correct.
[+] [-] jcadam|8 years ago|reply
Have to agree with all of this. I've taken Andrew Ng's Machine Learning course (only time I've paid for a 'verified' certificate), and found it a great overview of ML, though I'm not sure I'd feel comfortable telling anyone I have a good understanding of ML :)
Odersky's FP in Scala was actually the first Coursera course I took (during its initial run, I think). -- I also found the follow up Reactive Programming course to be excellent as well.
[+] [-] carusooneliner|8 years ago|reply
Couldn't agree more. I took this course in 2011 but didn't have a need for working with databases until 2014. Three years after I took the course I was able to jump in and work fluently on databases -- Mongo, Sqlite, Firebase, etc. The least I can say is the course helped me internalize database concepts.
This course has the right amount of handholding yet challenges you enough so you acquire long-lasting skills.
[+] [-] grblovrflowerrr|8 years ago|reply
[1] https://www.notion.so/MOOCs-recommended-by-Hacker-News-e1070...
EDIT: the previous link pointed to one of the headers in the page, instead of the page itself.
[+] [-] microtherion|8 years ago|reply
[+] [-] dmytrish|8 years ago|reply
– the first course in the specialization has a very good and engaging start, but the gap between lectures and problems widens quickly after that (maybe that's why the author boasts about a "challenging" course "not for everyone"). I'm hesitant to take the next course of the specialization.
[+] [-] cr0sh|8 years ago|reply
It's good to hear that this MOOC is still so well thought of since I first took it; for me, it was the first course I took that made me really understand how a neural network and back prop actually worked.
When I took the course, it was in 2011 - and was known as "ML Class"; yep - I was among the first "beta tester guinea pigs" of the course. It was fun and amazing to participate in.
One of the early participants was even inspired to replicate CMU's ALVINN self-driving vehicle in miniature:
http://blog.davidsingleton.org/nnrccar/
[+] [-] methehack|8 years ago|reply
[+] [-] bluetwo|8 years ago|reply
This course gave me professor envy and made me up my game. So well done.
[+] [-] microtherion|8 years ago|reply
Other music MOOCs I enjoyed:
The Berklee "Developing your Musicianship" series on Coursera taught by George W. Russell. Started off thinking this was too elementary, but the ear training is valuable, and I learned a lot about the use of diatonic chords, and even the few simple patterns he taught improved my song writing enormously.
The Berklee "Jazz Improvisation" class taught by Gary Burton. Very cool to be taught by a living legend, and his selection of songs was refreshingly modern. On the down side, skill levels of the students varied widely, so peer review was more miss than hit.
[+] [-] lr4444lr|8 years ago|reply
[+] [-] calenti|8 years ago|reply
[+] [-] runevault|8 years ago|reply
[+] [-] carusooneliner|8 years ago|reply
I wanted to invest better so I took this course to learn the basics of financial markets (I'm a software guy and have zero training in finance). After taking it, not only do I have the basics nailed down but have gained a massive appreciation of finance as a technology that, at its best, mitigates risk and advances society.
Shiller is an authority on the topic, having won a Nobel Prize in Economics no less. His penchant for financial market history and human behavior angle on things is a massive plus for this course. I'd say the course is useful education for entrepreneurs and curious folks alike.
[+] [-] dannygarcia|8 years ago|reply
Understanding how economies work[1], how financial service companies sell products, theories behind volatility and market forces, and how simple portfolio management can be goes a long way to improving an individuals ability to efficiently self-manage their finances.
1. https://www.youtube.com/watch?v=PHe0bXAIuk0
[+] [-] jadbox|8 years ago|reply
[+] [-] dfield|8 years ago|reply
[+] [-] antman|8 years ago|reply
Data Science
Introduction to Probability - The Science of Uncertainty,math oriented MIT/EDX Difficulty:5/5 Videos:5/5 Material and exercises:5/5 Usefulness: 5/5
Learning from Data, math oriented formerly Caltech/EDX now on caltech, check the exercises and you will see the difference in quality with Andrew Ng: Difficulty:4/5 Videos:4:5 Material and exercises:5:5 Usefulness:3/5
The Analytics Edge - Bertsimas MIT/EDX.You will learn practical stuff in R includes a kaggle competition. Difficulty:3/5 Videos:4/5 Material and exercises:6/5 Usefulness:6/5
Computational Probability and Inference MIT/EDX Computational probabilty using python. Difficulty:2/5 Videos:3/5 Material and exercises:6/5 Usefulness:5/5
Basic Modeling for Discrete Optimization: Uses an easy to learn language called minizinc which has multiple backends and is useful for those types of problems. VERY pleasant to watch videos. Difficulty:2/5 Videos:4/5 Material and exercises:3/5 Usefulness:5/5
Deep learning: deeplearning.ai coursera and fast.ai for more practical stuff.
Non data science:
I have not done the exercises on these just watched them:
Learning how to learn: Life changing I wish it existed many years ago.
Influencing People: Puts things into perspective. Makes you ponder about morality
Roman Architecture: Includes the "why" it is like the old "who moved my cheese" book, but in roman architecture edition.
Explaining European Paintings, 1400 to 1800: What it says on the tin.
Economics of money and Banking: In all tuthe courses I have listed the professors are very good. But this guy.... Makes a difficult subject so approachable and watching the news becomes as painful as watching a train full of passengers going to broken bridge
I am sure I have forgotten others
MOOCs have changed my life, financially and in other ways. I thank all the people involved.
[+] [-] carlosgg|8 years ago|reply
[+] [-] SiVal|8 years ago|reply
[+] [-] gmiller123456|8 years ago|reply
Udacity's Differential Equations course was pretty awesome too. I had taken Calculus previously, but I believe it's pretty approachable even if you haven't. The homework was very well designed, and involved fun problems like computing gravitational slingshots and curing diseases.
Coursera's "The Unwritten Constitution", also has a similar "The Written Constitution". Both are pretty awesome and really gives an in depth view of what the constitution is about (spoiler alert: it's about slavery), and even points out holes that haven't been challenged yet. Homework was writing essays and grading other people's, so not that well designed in that respect.
Coursera's "Coding the Matrix" is a Linear Algebra course. I took it the first time it was offered, and you pretty much had to buy the accompanying book to follow along. And the book unfortunately had a lot of "first version" issues. A lot of the homework wasn't explained very well, but it was all auto graded code. I think the issues with the book have been addressed with the second edition, not sure about the homework. I had already taken linear algebra before, so this was mostly a refresher, but even I found it hard to follow along in the last part, and never completed the last homework assignment.
On Youtube you can find "Fundamentals of Small Arms Weapons" from 1945. It shows how the action of a small arms rifle works. It starts as just a tube with a bullet, and works up to several different types of fully automatic actions. It's just a couple hours long.
[+] [-] rectang|8 years ago|reply
The tradeoff is that the student must debug during the exercises (an activity which is unrelated to the material), but it's worth it.
Regrettably, Coding the Matrix was taken down down along with a lot of first-generation Coursera courses. However, there's still the book, the website at http://codingthematrix.com and the the lectures from the Brown University version of the course:
https://cs.brown.edu/video/channels/coding-matrix-fall-2014
[+] [-] iooi|8 years ago|reply
Yes! Nand2Tetris came about before "MOOC" was even coined. It's a great course where you start building simple circuits in a hardware simulator, and eventually build a working Tetris game -- on hardware you built (running in a simulator), in an OS you wrote.
Highly, highly recommend it.
[+] [-] Patrick_Devine|8 years ago|reply
With Nand2Tetris, I just bought the book and worked through it without taking a class. I'd sketch out the hardware literally on the back of napkins and then try it in the simulator when I got home. It was incredibly fun, and I loved how it took everything down to first principles.
[+] [-] zweedeend|8 years ago|reply
[+] [-] cjauvin|8 years ago|reply
* Discrete Optimization: almost entirely problem-driven, very challenging and entertaining prof; https://www.coursera.org/learn/discrete-optimization
* Crypto I: very deep, thorough and crystal clear explanations; https://www.coursera.org/learn/crypto
* Computer Networks: excellent overall course covering a wide variety of topics; https://www.coursera.org/instructor/~517478, https://www.youtube.com/playlist?list=PLfgkuLYEOvGMWvHRgFAcj...
[+] [-] vector_rotcev|8 years ago|reply
By far and away the best learning course I've taken in my life as well, I wish it had been available before I had completed my formal education.
[+] [-] OldSchoolJohnny|8 years ago|reply
[+] [-] libdjml|8 years ago|reply
I own the book and have half-read it twice, it’s very underwhelming. At no point am I thinking “that’s going to change my way of doing X”
[+] [-] binaryanomaly|8 years ago|reply
[+] [-] wainstead|8 years ago|reply
[+] [-] rectang|8 years ago|reply
They are consistent, not very buggy, gamified, and consumable in small or large amounts. Sal Khan is a good communicator and the videos are decent, but it's the exercises that make Khan Academy exceptional.
[+] [-] komali2|8 years ago|reply
I hear tell Sal Khan is hiding out in the Bay Area somewhere, really wish I'd bump into him in a bar so I can grab his tab or something. Dude's a hero to me.
[+] [-] kmfrk|8 years ago|reply
I also loved that I would actually see Sal's videos come up as the top results for my calculus questions to illustrate things like matrix multiplication.
It's not often I feel like YouTube hits are as dead on the money for me as they used to be in thsoe days.
The 10-minute limit on videos at the time was to YouTube what the 140 character-limit was to Twitter.
[+] [-] saboot|8 years ago|reply
[+] [-] kmfrk|8 years ago|reply
I also loved that I would actually see Sal's videos come up as the top results for my calculus questions to illustrate things like matrix multiplication.
It's not often I feel like YouTube hits are as dead on the money for me as they used to be in thsoe days.
[+] [-] hyperpallium|8 years ago|reply
[+] [-] MadSudaca|8 years ago|reply
Taught by Prof. Scott E. Page, teaches about models in several fields and how they're used to aid thinking about complex issues by careful design and usage.
A couple of insights: all models are wrong but some are useful. Having many models about a situation to help your thinking is better than having only one, and much better than none. Complex models are not necessarily better than simple ones.
[+] [-] loganekz|8 years ago|reply
[1] - https://www.coursera.org/learn/progfun1
[+] [-] jpfr|8 years ago|reply
- Differential Equations from 2015: https://ocw.mit.edu/resources/res-18-009-learn-differential-...
- The original SICP recordings from 1986: https://ocw.mit.edu/courses/electrical-engineering-and-compu...
[+] [-] grdvnl|8 years ago|reply
https://www.coursera.org/learn/programming-languages
I see that they have split the course into 2 parts.
[+] [-] burlesona|8 years ago|reply
Fantastic course, more focused on theory than programming, but full of deeply fascinating commentary on what is knowledge, intelligence, learning, etc. and what does it mean for a program to demonstrate it (ie. what is AI anyway?).
My daughter was about 18 mo. old at the time I took the class, it was an outrageously awesome added bonus to watch a little human learn all the things I was trying to get a computer to learn at the same time.
[+] [-] Suncho|8 years ago|reply
https://www.coursera.org/learn/money-banking
It's just fantastic. He explains what money really is from the perspective of treating everyone as a bank. Also, lots of good history here including the history of central banking, the gold standard, and war finance.
Anyone who wants to understand money should take this course. It would be nice if more cryptocurrency enthusiasts learned this kind of monetary economics.
[+] [-] drxyzzy|8 years ago|reply
2. Astrophysics (on edX from Australian National University, 4-part series: https://www.edx.org/xseries/astrophysics) taught by Brian Schmidt and Paul Francis. Delightful. Plenty of math but mostly at undergrad level. A grand tour of current topics.
3. First Nights - Handel's Messiah and Baroque Oratorio (on edX from Harvard: https://www.edx.org/course/first-nights-messiah-harvardx-mus...) taught by Thomas Forrest Kelly. Historical perspective and structure of the music. I was hooked from the first lecture. One of a series of 5 outstanding courses in the "First Nights" series, this is my favorite.
So many great MOOCs, so little time.
[+] [-] rikkhill|8 years ago|reply
The subject matter covers a staggering breadth of topics, which can be characterised as either (a) fundamentals of philosophical reasoning, or (b) stuff that amateur internet-debaters think they understand but actually don't.
[1] - https://www.coursera.org/learn/understanding-arguments
[+] [-] gringoDan|8 years ago|reply
(Really anything by 3b1b)
[+] [-] imranq|8 years ago|reply
Other great courses: Learning to Learn, Irrational Psychology by Dan Ariely, and Algorithms by Sedgewick
Can someone recommend a good way to work with other students on MOOCs? I've taken many courses, but they aren't much better than just reading the textbook and working on a personal project, although the curation of content is valuable.
The relationship aspect is sorely missing from online courses. If there was an easy way to have a classroom setting with highly motivated peers each following the MOOC with a collaborative environment, then I would definitely want to sign up. You say that's what college is for? Well I've already graduated, signing up for random college classes is extremely expensive and the peer group is highly variable.
[+] [-] blcArmadillo|8 years ago|reply
[+] [-] rripken|8 years ago|reply
I found the Isbell+Littman combo to work so well that I also took the ML course. I know some people complain about their humor but it was perfect for me. I could listen to those two explain just about anything. I still LOL when I think about Littman saying to Isbell something like "are you trying to teach us something by making this lecture infinitely long?" Who knew RL could be funny?
[+] [-] phyller|8 years ago|reply