She is a wonderful teacher. When the very first MOOCs were introduced, I took AI taught by Sebastian Thrun, Peter Norvig and Databases taught by Jennifer Widom. I could contrast the teaching style of these experts. Jennifer Widom is approachable, methodical, breaks down complex topics into easily manageable chunks and pushes you hard with attainable goals. The other two geniuses (Norvig and Thrun) will present something that you will have struggle (with frustrations because denial isn't an option with them) to get it. I loved her way of teaching and I am glad she taking on responsible roles.
In these sets of MOOCs, there was also Machine Learning taught by Andrew Ng and I had your same Widom like experience with Andrew Ng. After taking so many courses in life - physically and remotely - I can safely say that Andrew Ng is the best teacher I've come across. The best part was how we actually gave insights in Machine Learning rather than leaving it like something esoteric. It changed my perspective and added a lot of value to my professional career.
Recently, I tried to take the Neural Networks from Jeff Hinton and I realized how much I missed Ng's lectures.
I took Geoff Hinton's Coursera course about 3 years ago, and it remains my favourite online course I've ever taken. While I do understand that it may have felt a bit theoretical and heavy, I also think that this theory is more important to understand in neural networks than in other areas of ML e.g. for debugging issues. I also loved his wry sense of humour and turn of phrase.
I felt that Andrew Ng left out a lot of the details that I was curious about. For example, I remember he introduces logistic regression and cross-entropy, but he kind of just writes down the equations and says they differentiate nicely, he never explains where they actually come from, or even points the interested viewer where to look for more information (short answer : information theory, also this post https://terrytao.wordpress.com/2016/06/01/how-to-assign-part... is a great explanation for cross entropy)
> While I do understand that it may have felt a bit theoretical and heavy, I also think that this theory is more important to understand in neural networks than in other areas of ML e.g. for debugging issues.
This is the same argument people gave for Machine Learning but Andrew Ng showed us otherwise.
> I felt that Andrew Ng left out a lot of the details that I was curious about.
This is exactly the beauty of Ng's ML course. The fact that Andrew Ng made you curious makes CS229A (Applications of Machine Learning) a very successful course. Now, if you want to satiate that curiosity I will recommend you CS229 [0]. It contains all the theory and math and YET that course is as beautiful as CS229A. This further proves the point that you do not need theory to be boring to achieve your objectives. If you want to teach something, you will find a way. Au contraire, Jeff Hinton's course makes me not want to even touch or understand Neural Networks again. FYI, I have a Ph.D in Computer Science and I work as a Data Scientist. If this is my view, imagine what it would be like beyond the us elites?
> FYI, I have a Ph.D in Computer Science and I work as a Data Scientist. If this is my view, imagine what it would be like beyond the us elites?
The probability, linear algebra and calculus material required to understand this should be covered in undergraduate courses. Which part exactly did you find so hard?
This isn't about the content but the structure and explaining the intuition behind algorithms. There are insights behind every algorithm which needs to be put down explicitly in simple English; not mathematical symbols. A course should be accessible to everyone given the prerequisites. Just because a course is easy for me, does not mean its a good course. The Hinton course is not palatable and you can clearly see that in the discussion forums.
Andrew Ng's CS229 course goes beyond the mathematics and explains the magic behind these algorithms. Unfortunately, I know many people who use the mathematics trope to not share these insights and keep them closely guarded to their chest.
I remember taking Ng's course soon after having taken a similar course. While his offered insights were occasionally useful, I found a number of them distracting, especially when they differed substantially from what I had learned in the other course. I found most of his explanations simple but some of them were unsatisfactory, as the suggested motivations did not imply the taught solutions. This was forgivable where I knew of several alternatives, but not when the information was new to me.
His class left me thinking I was able to apply problems to solutions, not the other way around. I will agree that he was a better teacher than Norvig and Thrun, and more engaging (though less holistic) than Widom.
I've had exactly the same struggle. Andrew Ng was truly awesome, better than any lecturer I had. Jeff Hinton's course is just a real struggle to complete.
It's more than just the lectures, the thought and relevance that goes into the problem sets is a big difference too.
It's great to hear that Jennifer Widom's course is on a similar level. Perhaps I should dust up on my database knowledge.
I agree with you about the exceptional quality of that course. I did not take the other two earliest large online courses, but I have taken few dozen since then. Compared to MOOCs at different schools, I found her teaching style more similar to those offered by instructors at MIT than those offered by instructors at Stanford.
I think your description is right, too. Methodical and easily manageable without a loss in rigor.
Most people who struggle to learn to code don't do well with expected lightbulb moments, but I've seen the same people do wonderfully when presented with methodical steps as building a trunk they can branch off of.
I owe a lot to her and to Thrun as well. I took Intro to Stats by Thrun and Intro to Databases by Widom when I wanted to brush up on my skills before interviewing and both have wonderful teaching style. Starting at very slow pace and smoothly and methodically going into depth of the subject matter.
Thanks for sharing that link. On a tangent, I love these old university personal pages. In an age of impermanence, it's endearing to stumble upon web pages still up 20 years after they were made.
I've used the Arroyo Seco River Hike guide from another Stanford alum quite a bit:
edit: update looks like they're part of a community of nudist hikers, the South Bay Naturists. Nothing wrong with that, though bold to post about it on your work website.
I have taken also other MOOCs (Machine Learning, Probabilistic Graphical Models, Functional Programming Principles in Scala, ...) and Widom's databases MOOC was by far the best planned, best designed and best executed.
There was quite a lot of homework, but it was all well planned and served a purpose, you could do all the SQL, Xpath, JSON, ... query exercises in the browser without needing to install anything, and the system gave good feedback when you did something wrong. That environment was very well implemented.
The course was packed with theory and practice, there was minimal extra chatter in the video lectures, almost every minute of the lectures was used to cover the material, and the lectures provided just the right amount of theory for doing the exercises, and the exercises covered almost everything from the lectures. Good video lectures are more dense, and proceed faster than live lectures, because when you miss something, you can always rewind and watch the last minutes again.
Very impressive credentials and a well-deserved appointment. Being appointed Dean of the School of Engineering at one of the world's most prestigious universities has to be close to the top of career achievements for an academic.
I can honestly say that taking Widom's Intro DB class in the late 90s set my entire career trajectory. It may have been the most influential class I took at Stanford. I am very grateful for her teachings and guidance.
How much influence do deans have at Stanford? I think she's a great teacher, but from my experience, your teaching ability is irrelevant when it comes to your responsibilities as Dean.
I have no experience being a dean. I do have experience being managed by one. I personally didn't see any correlation in their effectiveness and their popularity as a teacher. If you have any evidence stating there's a correlation I'd love to read it.
I'm sure there's some correlation between teaching ability and money raising, one of the more important aspects of being dean. They both stem from being a good people person.
Maybe she was promoted to Dean because she's incredibly well-rounded, has a solid vision, is of great character, has cultivated numerous productive working relationships and can fund raise like no other. She can be all that and a great teacher.
She was the chair of the cs department for two terms and did a fantastic job by all measures. Teaching skills aren't why she is qualified. Past administrative experience and success are why she is qualified.
It's an abuse of HN to use it primarily for political arguments, and especially to do so in trollish ways. Since we warned you repeatedly, we've banned this account.
Firstly, Standford does not have an affirmative action hiring policy with regards to their dean of education, so this ought to be moot.
Secondly, in a field with so many incredibly talented and well recommended individuals, hiring is hard and there's no way to objectively determine who is the best. So we try hard to filter down candidates, and in the end pick someone knowing that at the very least we've chosen someone utterly amazing.
If only we could go back to the Eden of the past when every position was earned entirely on merit and no one ever benefited from social connections or say systematic discrimination against other groups.
In the case that a wealthy white man had gotten the job, we would see someone else shout patriarchy instead of affirmative action. Hopefully that would have the same result here on HN.
I was thinking something similar! Just don't know these days. She may have gotten the position on merit, or not.
I feel bad. But what's the alternative: Bullshit & say I'm sure her being a woman has nothing to do with the promotion? I just can't say that with any certainty.
I guess affirmative action is a double-edged sword.
Does it bother you if a male candidate gets a position that maybe there's a better qualified female candidate who wasn't considered because "maybe she wants to start a family" or some bullshit? Do you let that (potential) bias affect your judgment of whether the guy "deserved" it?
Biases exist. Meritocracy is an illusion. Deal with it.
I think what's getting lost in this discussion is that Affirmative Action isn't a trigger for "hire a woman over the more qualified man".
All it mandates is that if on paper two candidates are equal scores, your process should pick the female/minority candidate. Historically, if you left it up to people in the organisation they'd go with their "gut" feelings and pick whomever was of a similar background to them.
If two candidates aren't equal, you always pick the better candidate.
> Does it bother you if a male candidate gets a position that maybe there's a better qualified female candidate who wasn't considered because "maybe she wants to start a family" or some bullshit?
Yes. I try and have a coherent worldview. And I don't think discrimination is useful for productivity.
Your statements reveal more about your insecurity than her competence. I suspect you don't know anything about her but still feel qualified to cast aspersions on her competence. Why is that?
And the thermo guys are out. Might be a good time to recognize your University runs an isolated HCI group in it's CS department and about 4 different "Design" groups within the School of Engineering that are _NOT_ connected to HCI in anyway...
Meanwhile the only course I know of at Stanford that teaches Arduino is at CCRMA.stanford.edu
> Meanwhile the only course I know of at Stanford that teaches Arduino is at CCRMA.stanford.edu
I don't understand why this is an issue. Why would you want that when Stanford and almost every other school offers Intro to Electrical Engineering? It will contain everything you need to know to hack away at Arduino and a lot more.It's EE40 as I recall and after the first two weeks of that class you should be able to play around with Arduino. Why would you want to waste thousands of dollars in tuition for knowledge you can pick up in a weekend at your local hobbyist Meetup for $20 or less
Do you also bemoan the fact that Stanford doesn't offer a class in how to use Google or an Intro to Typing?
Preface: I hold a BSE Product Design 2009, Stanford. I conducted research at the CDR. I took courses at the d.school and CCRMA. I attended weekly HCI research meetings for 3 weeks and saw Ed Catmull present to the HCI group once.
THIS IS AN IMPORTANT DESIGN ISSUE.
Please understand that Stanford's School of Engineering pretty much owns and grossly mis-manages the teaching and cooperation amongst different groups with an interest in DESIGN that there are 6 programs in design that aren't interrelated, HCI belongs to Computer Science, Design Impact is it's own program, the d.school doesn't grant degrees, I'm not sure what the "Design Group" in Mechanical Engineering is, but it is related to the only PhD design Program at Stanford, the CDR, and product design have no formal linkages. It would not be common for a Product Design student to ever know or encounter a CS HCI student, except by coincidence or other interest.
This represents an important change in leadership in the school of engineering. Previously it was my understanding the SoE was being run by the thermo guys who are totally clueless to the importance of Design. Stanford Engineering owns Stanford Design and is an extremely fragmented program which completely ISOLATES the Computer Science Department (a part of the School of Engineering) has ZERO ties to 5 other DESIGN programs at STANFORD within the SoE.[1][2][3][4][5][6]
Lastly the only course that teaches anything to do with arduino is at CCRMA.[6]
[1] Center for Design Research cdr.stanford.edu
[1] HCI hci.stanford.edu/
[2] Design Impact – A Master of Science in Engineering Program at Stanford designimpact.stanford.edu/
First, I'm not clear how your comment is relevant to the post.
Second, I think the "design" situation at Stanford would make a bit more sense to you if you took some time to better understand the academic field of HCI, of which design is just a small sub-area. Browsing the list of papers at last year's CHI is a good place to start [1].
Third, several HCI students are co-advised by design faculty in mechanical engineering and HCI faculty. Several HCI students also both take classes and physically work out of the d.school. CCRMA members routinely attend HCI lunches.
Fourth, product design is no more relevant to the academic research that takes place in HCI than it is for the many other sub-fields of computer science for which building systems is a major aspect.
This is relevant because design is being controlled by the school of engineering and they are not managing it well. There are too many non-related groups. Yes some extraordinary students work between these silos, but it would be far better for the School of Engineering to restructure it's design groups or to let Design become a school outside of the School of Engineering.
There are no formal connections between the groups. Yes, I am an example of a student who attended CCRMA, Winograd's HCI group, CDR, d.school.
I graduated with 185 credits, 15 of which were basically useless nothingness because I couldn't actually enroll in the courses I was taking and professors had to admit me to their special research courses like ME 293Q.
It is a disaster in terms of organizational structure and it's all due to SoE Politics.
It's relevant because now the Dean seems to be someone who has an idea about the modern world and isn't focused on hard sciences like thermodynamics or materials science.
Also why are you referencing academic papers? PhD programs are for producing professors, that's not the topic of discussion. We're talking about education.
Last I prove conclusively that Product Design is indeed relevant and thus refute your wide, incorrect, sweeping assertion. Again I personally know a Stanford product design student who conducted academic research in HCI.[1] Please do not make such sweeping assertions when you're not actually informed. I know its appealing and sounds nice, but its totally inaccurate. Even you yourself speak of students working between the Silos. To ascribe hate onto Product Design seems like misplaced anger.
To be clear, I am not claiming that Product Design has no place in HCI. I'm claiming that its just as relevant to many other areas of computer science as it is to HCI.
