#39 overallEvergreen
Intro to Artificial Intelligence
Udacity
9 recommenders · 9 threads · recommended in 6 years, 2012–2021 · score 14.1
Udacity's introductory artificial intelligence course (CS271), taught by Sebastian Thrun and Peter Norvig, originally started as the 2011 Stanford-sponsored AI Class. It covers the foundations of AI, with commenters describing it as a free, self-paced online course.
Commenters call it an excellent, clear introduction to AI, praising Peter Norvig's teaching, though some note errors and a lack of polish.
Machine Learning & AIbeginnerfreevideo lectures#artificial intelligence#search#probability#norvig#thrun
Recommendations by year
20062026
AI != ML
For AI, I would take the Udacity AI courses.
For ML, I would take the Udacity ML courses.
I take a lot of different online courses, I have no affiliation with Udacity, but their courses are just too good.
I studied AI (focused on ML) in a decent grad school (and I like to think I had the best teachers there), and I think the quality of the courses is comparable.
To think that i took the original ml-course Ng) and ai-course (Norvig) years ago. Great courses.
Currently I find Udacity model better than Coursera: I can get a course and take it in my own time without having to stick to a schedule. Also the fact that I pay for what I consume. I personally dont like subscription model for these things you may use once or twice every yesr
No.
I recommend the ML course by Andrew Ng (I did it on coursera) and the AI course by Sebastian Thrun and Peter Norvig.
By all means get some "background knowledge" (linear algebra, statistics, calculus etc), play around with libraries and follow some MOOC, but primarily I'd suggest you go get yourself a post-graduate degree from a brick-and-mortar university, and in a course called "Data Science" or "Artificial Intelligence" and the like.
You can learn on your own, of course, but a university course will focus your learning, provide rich feedback, and give you a strong foundation on which to build. You'll also get to learn from other students, which is not often the case in MOOCs. And there's nothing like having a teacher on your payroll (which is essentially what paying for a course is) to answer your questions, clarify obscure areas in books and generally support you throughout the course.
For the record- I did exactly what I say above. After five years working in the industry as a dev, I took a Masters part-time, sponsored by my employer. I think I got a good foundation as I say above, and I certainly didn't have the time, or the focus, to learn the same things on my own.
And I did try on my own, with MOOCs-and-books for a while. I did learn useful stuff (the introductory AI course from Udacity for instance, was really helpful) but after starting the Masters it felt like all this time I'd been crawling along without aim, and now I was running.
I enjoyed the Udacity AI course, but I was repeatedly frustrated by the many errors that were never corrected and especially Peter Norvig's nails on blackboard scribbling sounds with his felt tip. Am I the only one who could hear this?
Overall, I think there was a real lack of attention to detail after an excellent job on getting the big picture right.
That said, MOOCs have revolutionized my ability to keep up with technology trends and I spend 1-2 hours daily on Coursera and Udacity. I do not care whatsoever about certificates or completing courses. I just care about raw knowledge acquisition and reviewing previously learned material (i.e. refreshing my calculus, statistics, algorithms, and linear algebra skills).
I personally think the future of this business will involve celebrity teachers like Andrew Ng and Sebastian Thrun putting together more polished versions of their courses and then allowing a cottage industry to grow around tutoring people through them at their own pace rather than shoehorning a freestyle approach to learning into effective semesters and quarters.
If this is infotainment, it sure beats the likes of Fox News and MSNBC.
This is neat! Fantastic, even! The math is less theoretical and more systems oriented. The choice of python, modern psuedocode that runs, is great, too. The naive Bayes chapter is useful, too. One might want to look at Udacity's AI course for more info about this topic or as a supplement. Bayes seems to be one of those things where the math is short and difficult; I've been reading about it recently, myself. Just practice, I guess. To engineer stuff with it you may not need to understand it perfectly (until you get bugs ;). Anyways, it's still good. It's a hard topic and Bayes law/tricks appear in AI often so it's worth knowing more about.
Thank you, Ron Zacharski!
(disclaimer: you do not want my opinion regarding any topic).
Definitely a solid foundation in linear algebra and statistics (mostly Bayesian) are necessary for understanding how the algorithms work. Check out the wiki portals (http://en.wikipedia.org/wiki/Machine_learning) and (http://en.wikipedia.org/wiki/Artificial_intelligence) for overviews of the most common approaches.
Also, Andrew Ng's coursera course on machine learning is amazing (https://www.coursera.org/course/ml) as well as Norvig and Thrun's Udacity course on AI (https://www.udacity.com/course/cs271)
It would be great if they can coordinate their classes with Coursera. I signed up for algo-class.org, but now I see that Udacity will also have similar class.
Edit: I completed ML (Coursera) and AI Intro (Udacity) classes and I have to admit that execution of Coursera was way better. Not sure how things will be for algo classes.
Not just the ML course. I signed up for one on human computer interaction, high tech entrepreneurship and lean startup and they have all been delayed indefinitely. It's a pity as I did Sebastian Thun's AI course and it was great and I was looking forward to these others... so the consumer isn't always winning as these companies fight it out, I think they should just put the material out for free it would create many new startups!
I don't doubt that there are people working at EdX / Coursera with a passion for education. I just think maybe these companies are moving in a direction that is at odds with the goal of providing free education, everywhere, to everyone, at any stage in their life.
I enrolled in some of the earliest MOOCs. Sebastian Thrun's original ai-class.com which now redirects to Udacity. I took the first iteration of Andrew Ng's "Machine Learning" on Coursera, as well as Geoffrey Hinton's original NNML course. Back then, everything was open. Course materials were shared freely, and the archives were available for years after the course concluded. There was an autograder for coding assignments that didn't get in your way too much.
Slowly, more and more roadblocks were put in place.
What was your experience like at Coursera? Did you get a chance to see how decisions about the UI and structure of courses were made? Did you get a sense of how much the marketing / business side of things interfered with the education side?
For who? Maybe these sites have created a product that works well for a certain niche of people, and they've hyper-optimized for that. Great. But that's not really the dream we all had for it ten years ago.
Like I said in a sibling comment: I've already been through school, and already know my own learning process. I find that the practices Coursera / EdX actively get in the way of my learning.
Language changes. Most people include in their meaning of "dark pattern" things like "artificially restricting you from performing actions that the website is fully capable of performing, with dubious or justification or malicious intent".
I don't think EdX is malicious, just that their reasons for restricting usage of course materials are dubious, and conflict with their stated mission.
Why can't it be a content library? I learn a lot at libraries!
This structure helps some people, sure. But some people like me are not full-time students. Some weeks I have lots of time to dig in, other weeks I don't have time to even watch a lecture. Moreover, I'm learning for myself, not for credentials, so why should I care what a website thinks of my progress?
I don't have definitive proof, but every time I visit the EdX or Coursera sites it just so happens that the exact course I was searching for started within a week of the current date. Maybe I'm being paranoid.
This was mostly a joke :)
Again, I'm not a student. I trust my own learning process, which is impeded by constant quizzes. I'm doing this to broaden my knowledge. I don't have time to enroll in a college class, but I have time to listen to a few lectures when doing dishes, and read a couple book chapters per week.
Coursera and others are technically capable of opening up their service to this use case -- it doesn't cost them anything -- so why not do it?
In a certain sense, online education is thriving! There are tons of video lectures on YouTube available for free and I can easily pirate any textbook I want to with a quick Google search. It's just that Coursera / EdX / etc don't really fit into that for me. I really wish they did.
I second this suggestion; I took that course when it was called "ML Class" during the Fall of 2011 (yep, I was one of the guinea pigs for what became one of the first courses of Coursera). It was an excellent course.
Here's an example of what one student of the ML Class built, after being inspired by what he was learning and videos that played during the course:
https://blog.davidsingleton.org/nnrccar/
It kinda shocked me at the time, because I knew quite a bit about ALVINN from books and articles I had read as a teenager in the 80s and 90s. This guy had created the same thing using a cell phone and a cheap RC vehicle! Ok, there was also an Arduino and computer involved - but it really hit home the fact that technology around neural networks had advanced quite a bit!
I also took the other course, "AI Class", but due to personal issues I had to drop out about halfway through.
The next year, after Udacity started, they introduced a course similar to AI Class called "How to Build Your Own Self-Driving Vehicle" (it's called something else today - something like "Robotics and Artificial Intelligence 302" or something like that).
That class was done in Python, and taught me even more about AI/ML - with a focus towards self-driving vehicles of course. Things I learned about that I struggled with or had no real concepts of before:
1. SLAM (Simultaneous Localization and Mapping) 2. Path Finding algorithms (A* and the like) 3. Kalman Filtering (what it is for, how it works) 4. PID Algorithm (how to implement and tune it) 5. More neural network stuff...
...and many other things. Another very excellent and free course to take if you're interested in learning this stuff.
Ditto for udacity. Although Peter Norvigs course is outstanding
Peter Norvig is probably my favorite online teacher, from the AI class in 2011 to his Design of Computer Programs on Udacity. His attitude is inviting, and his exposition clear.
The kerning in the PDF looks weird on my Mac, but it's really cool that this is available.
So far, yes - but that has a few caveats:
See - I have some background prior to this, and I think it biases me a bit. First, I was one of the cohort that took the Stanford-sponsored ML Class (Andrew Ng) and AI Class (Thrun/Norvig), in 2011. While I wasn't able to complete the AI Class (due to personal reasons), I did complete the ML Class.
Both of these courses are now offered by Udacity (AI Class) and Coursera (ML Class):
https://www.udacity.com/course/intro-to-artificial-intellige...
https://www.coursera.org/learn/machine-learning
If you have never done any of this before, I encourage you to look into these courses first. IIRC, they are both free and self-paced online. I honestly found the ML Class to be easier than the AI class when I took them - but that was before the founding of these two MOOC-focused companies, so the content may have changed or been made more understandable since then.
In fact, now that I think about it, I might try taking those courses again myself as a refresher!
After that (and kicking myself for dropping out of the AI Class - but I didn't have a real choice there at the time), in 2012 Udacity started, and because of (reasons...) they couldn't offer the AI Class as a course (while for some reason, Coursera could offer the ML Class - there must have been licensing issues or something) - so instead, they offered their CS373 course in 2012 (at the time, titled "How to Build Your Own Self-Driving Vehicle" or something like that - quite a lofty title):
https://www.udacity.com/course/artificial-intelligence-for-r...
I jumped at it - and completed it as well; I found it to be a great course, and while difficult, it was very enlightening on several fronts (for the first time, it clearly explained to me exactly how a Kalman filter and PID worked!).
So - I have that background, plus everything else I have read before then or since (AI/ML has been a side interest of mine since I was a child - I'm 43 now).
My suggestion if you are just starting would be to take the courses in roughly this order - and only after you are fairly comfortable with both linear algebra concepts (mainly vectors/matrices math - dot product and the like) and stats/probabilities. To a certain extent (and I have found this out with this current Udacity course), having a knowledge of some basic calculus concepts (derivatives mainly) will be of help - but so far, despite that minor handicap, I've been ok without that greater knowledge - but I do intend to learn it:
1. Coursera ML Class 2. Udacity AI Class 3. Udacity CS373 course 4. Udacity Self-Driving Car Engineer Nanodegree
I honestly think it will - but I also have over 25 years under my belt as a professional software developer/engineer. Ultimately, it - along with the other courses I took - will (and have) help me in having other tools and ideas to bring to bear on problems. Also - realize that this knowledge can apply to multiple domains - not just vehicles. Marketing, robotics, design - heck, you name it - all will need or do currently need people who understand machine learning techniques.
I believe you could, depending on your prior background. That said, don't think that these courses could ever substitute for graduate degree in ML - but I do think they could be a great stepping stone. I am actually planning on looking into getting my BA then Masters (hopefully) in Comp Sci after completing this course. Its something I should have done long ago, but better late than never, I guess! All I currently have is an associates from a tech school (worth almost nothing), and my high school diploma - but that, plus my willingness to constantly learn and stay ahead in my skills has never let me down career-wise! So I think having this ML experience will ultimately be a plus.
Worst-case scenario: I can use what I have learned in the development of a homebrew UGV (unmanned ground vehicle) I've been working at on and off for the past few years (mostly "off" - lol).
No problem, I hope my thoughts help - if you have other questions, PM me...
Peter Norvig's Artificial Intelligence:
http://www.amazon.com/Artificial-Intelligence-Modern-Approac...
Has plenty of examples in Python. You can also look at different Udacity courses. They have a couple dealing with ML with Python.
Bingo. If I remember correctly, Udacity also started with the excellent AI course by Sebastian Thrun.
After that you can take the AI course on Udacity.
Are you saying people who do theoretical work don't don't it in practical ways?
As an example, take Google's self-driving cars. Would you say the meat of their body of work theoretical? But would say the code they (Sebastian Thrun himself, and others involved in the project) write code that's not very practical, readable, maintainable or without tests? I would say what they do is theoretical work. And they write practical code for their theoretical work.
If you look at Udacity classes - I've watched Peter Norvig not Thrun's so I'll use that as a reference - Norvig places a lot of importance for tests, maintainability, readability and other practical things throughout his class. I would assume the code he writes (however little now, however much in the past) is very practical. Yet what he writes code for has a huge theoretical aspect to it. I'm saying one can have their body of work being theoretical by nature but that doesn't mean they don't write practical code.