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I agree totally. The first two sins of education are making simple things complicated and making complicated things simple. Ng was clear, and that is commendabl

by diiq 15y ago

I agree totally. The first two sins of education are making simple things complicated and making complicated things simple. Ng was clear, and that is commendable. My concern was not based on how difficult he made the material he presented seem. I was concerned that he did not present more difficult material. Vocabulary is very important. But in addition to knowing the meanings of words, its important to know why concepts are carved up the way they are. Why divide the world into supervised and unsupervised --- what does carving at that joint win us? Getting people interested is what the first week is for. The other eight, I feel, should try to satisfy the curiosity the first week sparked.

nessus42 15y ago
Vocabulary is very important. But in addition to knowing the meanings of words, its important to know why concepts are carved up the way they are. Why divide the world into supervised and unsupervised --- what does carving at that joint win us? I'm not sure that I get you. Why we have both supervised and unsupervised algorithms was made pretty clear to me in the class. And just what more difficult material would you have liked to seen? The real Stanford class is made more difficult largely by doing lots of difficult math proofs. I emphatically disagree with any assertion that this free class should be so math heavy. What would be the purpose of that for the intended audience? Sure, such work would make fine extra credit, but making effective use of algorithms rarely relies on on a ability to mathematically prove that they have the properties that they do. I think that a big final project, as is required in the real Stanford class, on the other hand, would have a lot of utility for the intended audience. But as that would be impossible to automatically grade, that's a non-starter.
- diiq 15y ago
  I agree with you that doing proofs isn't the most important thing --- though I might not be so adamant about avoiding it. My own experience with ML is so narrow, it's hard for me to say what else should have made an appearance --- I made up the example that you rightly called me on. I worked with a research group on a reinforcement-learning system with a silly name, and we used a whole messy pile of linear algebra. I guess I shouldn't have expected to recieve the same kind of grounding in all the ML topics. I agree that projects would make for a huge improvement. They are difficult, but no more difficult than they need to be, and practical. You're right, grading 20,000 of them would be madness. And maybe that's OK --- what's wrong with assigning work that won't be graded? (OK, there are lots of things wring with it, starting with motivating the student to do the work, and ending with the lack of quality feedback. But even suggesting projects for self-directed students might be enough to get a blog-ecosystem going. I don't know much about pedagogy --- I'll talk to some people who do, and maybe they'll dope-slap me into agreeing with you totally.) But I don't think we disagree particularly -- math should only appear when necessary to understanding. Easy things shouldn't be made difficult just for the sake of some kind of scholastic masochism. But difficult things should be attacked with vigor, and not nerfed for the sake of the audience.
  - nessus42 15y ago
    Now that I think about it, I do think that I heard somewhere that the free AI class has a final project. IIRC, it's a "challenge" that is the same for every student, and the solutions are ranked by how well they solve the problem according to some metric that can be measured automatically. Kind of like how Netflix set up a challenge like that with a million dollar prize, only in the AI class there was no cash prize. (I hear that they did send out requests for job interviews, though, as rewards.) This actually sounds like a great idea to me. Topcoder for AI. I'm sure something similar could be devised for the Machine Learning class. Now that I think about it, I'd actually be pretty surprised if they don't do something like this for future versions of the class. I've heard some people complain that the programming assignments in the Machine Learning class were too easy. A specific complaint is that they provided all the equations and explication you needed right in the homework statement rather than having to remember it from the lectures. Personally, I find this approach to be the best way to learn. My favorite approach to learning has always been "workbook" based, where the lessons and problems to solve are in self-contained lessons. Give me material like that and I can learn anything. There are entire classes at MIT that I did extremely well in because they were workbook based. And I took Organic Chemistry and got an A+ in the first half of the class because it was workbook based. They thought I was a genius. Then the second half of the class used the more traditional approach of reading 100 pages a week of terribly boring and dense textbook. I got a D- in that half. Fortunately, it averaged to a C and I passed the class, but if the entire class had been workbook based, maybe I'd be doing something great with Computational Chemistry at the moment. Back to the actual ML class, I've only completed the first few programming exercises as of yet, as I was also taking the database class, which actually turned out to be a lot of work. I've heard that in the ML class, the programming exercises become progressively spoon-fed, and ultimately not much of a challenge. That's not good, if true. While I do think that all the information you need should be at hand, you should still be given challenges that make you think. All the thinking should not be done for you.

by diiq 15y ago

nessus42 15y ago

Vocabulary is very important. But in addition to knowing the meanings of words, its important to know why concepts are carved up the way they are. Why divide the world into supervised and unsupervised --- what does carving at that joint win us? I'm not sure that I get you. Why we have both supervised and unsupervised algorithms was made pretty clear to me in the class. And just what more difficult material would you have liked to seen? The real Stanford class is made more difficult largely by doing lots of difficult math proofs. I emphatically disagree with any assertion that this free class should be so math heavy. What would be the purpose of that for the intended audience? Sure, such work would make fine extra credit, but making effective use of algorithms rarely relies on on a ability to mathematically prove that they have the properties that they do. I think that a big final project, as is required in the real Stanford class, on the other hand, would have a lot of utility for the intended audience. But as that would be impossible to automatically grade, that's a non-starter.

diiq 15y ago
I agree with you that doing proofs isn't the most important thing --- though I might not be so adamant about avoiding it. My own experience with ML is so narrow, it's hard for me to say what else should have made an appearance --- I made up the example that you rightly called me on. I worked with a research group on a reinforcement-learning system with a silly name, and we used a whole messy pile of linear algebra. I guess I shouldn't have expected to recieve the same kind of grounding in all the ML topics. I agree that projects would make for a huge improvement. They are difficult, but no more difficult than they need to be, and practical. You're right, grading 20,000 of them would be madness. And maybe that's OK --- what's wrong with assigning work that won't be graded? (OK, there are lots of things wring with it, starting with motivating the student to do the work, and ending with the lack of quality feedback. But even suggesting projects for self-directed students might be enough to get a blog-ecosystem going. I don't know much about pedagogy --- I'll talk to some people who do, and maybe they'll dope-slap me into agreeing with you totally.) But I don't think we disagree particularly -- math should only appear when necessary to understanding. Easy things shouldn't be made difficult just for the sake of some kind of scholastic masochism. But difficult things should be attacked with vigor, and not nerfed for the sake of the audience.
- nessus42 15y ago
  Now that I think about it, I do think that I heard somewhere that the free AI class has a final project. IIRC, it's a "challenge" that is the same for every student, and the solutions are ranked by how well they solve the problem according to some metric that can be measured automatically. Kind of like how Netflix set up a challenge like that with a million dollar prize, only in the AI class there was no cash prize. (I hear that they did send out requests for job interviews, though, as rewards.) This actually sounds like a great idea to me. Topcoder for AI. I'm sure something similar could be devised for the Machine Learning class. Now that I think about it, I'd actually be pretty surprised if they don't do something like this for future versions of the class. I've heard some people complain that the programming assignments in the Machine Learning class were too easy. A specific complaint is that they provided all the equations and explication you needed right in the homework statement rather than having to remember it from the lectures. Personally, I find this approach to be the best way to learn. My favorite approach to learning has always been "workbook" based, where the lessons and problems to solve are in self-contained lessons. Give me material like that and I can learn anything. There are entire classes at MIT that I did extremely well in because they were workbook based. And I took Organic Chemistry and got an A+ in the first half of the class because it was workbook based. They thought I was a genius. Then the second half of the class used the more traditional approach of reading 100 pages a week of terribly boring and dense textbook. I got a D- in that half. Fortunately, it averaged to a C and I passed the class, but if the entire class had been workbook based, maybe I'd be doing something great with Computational Chemistry at the moment. Back to the actual ML class, I've only completed the first few programming exercises as of yet, as I was also taking the database class, which actually turned out to be a lot of work. I've heard that in the ML class, the programming exercises become progressively spoon-fed, and ultimately not much of a challenge. That's not good, if true. While I do think that all the information you need should be at hand, you should still be given challenges that make you think. All the thinking should not be done for you.