What are your plans between ACL and ICML?

I'll tell you what they should be: attending the Symposium on Machine Learning in Speech and Language Processing, jointly sponsored by IMLS, ICML and ISCA, that I'm co-organizing with Dan Roth, Geoff Zweig and Joseph Keshet (the exact date is June 27, in the same venue as ICML in Bellevue, Washington).  So far we've got a great list of invited speakers from all of these areas, including Mark Steedman, Stan Chen, Yoshua Bengio, Lawrence Saul, Sanjoy Dasgupta and more.  (See the web page for more details.)  We'll also be organizing some sort of day trips (together with the local organizers of ICML) for people who want to join!  You should also consider submitting papers (deadline is April 15).

I know I said a month ago that I would blog more.  I guess that turned out to be a lie.  The problem is that I only have so much patience for writing and I've been spending a lot of time writing non-blog things recently.  I decided to use my time-off-teaching doing something far more time consuming than teaching. This has been a wonderously useful exercise for me and I hope that, perhaps starting in 2012, other people can take advantage of this work.

How I teach machine learning

I've had discussions about this with tons of people, and it seems like my approach is fairly odd. So I thought I'd blog about it because I've put a lot of thought into it over the past four offerings of the machine learning course here at Utah.

At a high level, if there is one thing I want them to remember after the semester is over it's the idea of generalization and how it relates to function complexity. That's it. Now, more operationally, I'd like them to learn SVMs (and kernels) and EM for generative models.

In my opinion, the whole tenor of the class is set by how it starts. Here's how I start.

1. Decision trees. No entropy. No mutual information. Just decision trees based on classification accuracy. Why? Because the point isn't to teach them decision trees. The point is to get as quickly as possible to the point where we can talk about things like generalization and function complexity. Why decision trees? Because EVERYONE gets them. They're so intuitive. And analogies to 20 questions abound. We also talk about the who notion of data being drawn from a distribution and what it means to predict well in the future.
   
   
2. Nearest neighbor classifiers. No radial basis functions, no locally weighted methods, etc. Why? Because I want to introduce the idea of thinking of data as points in high dimensional space. This is a big step for a lot of people, and one that takes some getting used to. We then do k-nearest neighbor and relate it to generalization, overfitting, etc. The punch line of this section is the idea of a decision boundary and the complexity of decision boundaries.
   
   
3. Linear algebra and calculus review. At this point, they're ready to see why these things matter. We've already hinted at learning as some sort of optimization (via decision trees) and data in high dimensions, hence calculus and linear algebra. Note: no real probability here.
   
   
4. Linear classifiers as methods for directly optimizing a decision boundary. We start with 0-1 loss and then move to perceptron. Students love perceptron because it's so procedural.
   

The rest follows mostly as almost every other machine learning course out there. But IMO these first four days are crucial. I've tried (in the past) starting with linear regression or linear classification and it's just a disaster. You spend too much time talking about unimportant stuff. The intro with error-based decision trees moving to kNN is amazingly useful.

The sad thing is that there are basically no books that follow any order even remotely like this. Except...drum roll... it's actually not far from what Mitchell's book does. Except he does kNN much later. It's really depressing how bad most machine learning books are from a pedagogical perspective... you'd think that in 12 years someone would have written something that works better.

On top of that, the most recent time I taught ML, I structured everything around recommender systems. You can actually make it all work, and it's a lot of fun. We actually did recommender systems for classes here at the U (I had about 90-odd students from AI the previous semester fill out ratings on classes they'd taken in the past). The data was a bit sparse, but I think it was a lot of fun.

The other thing I change most recently that I'm very happy with is that I have a full project on feature engineering. (It ties in to the course recommender system idea.) Why? Because most people who take ML, if they ever use it at all, will need to do this. It's maybe one of the most important things that they'll have to learn. We should try to teach it. Again, something that no one ever talks about in books.

Anyway, that's my set of tricks. If you have some that you particularly like, feel free to share!

Teaching machine translation

Last Fall (2007), I taught an Applications of NLP course to a 50/50 mix of grads and senior undergrads. It was modeled partially after a course that I took from Kevin Knight while a grad student. It was essentially 1/3 on finite state methods for things like NER and tagging, then 1/3 on machine translation, then 1/3 on question answering and summarization. Overall, the course went over fairly well.

I had a significant problem, however, teaching machine translation. Here's the problem.

Students knew all about FSTs because we used them to do all the named-entity stuff in the first third of class. This enabled us to talk about things like IBM model 1 and the HMM model. (There's a technical difficult here, namely dealing with incomplete data, so we talk about EM a little bit.) We discuss, but they don't actually make use of, higher order MT models.

Now, we all know that there's a lot more to MT than model 4 (even limiting oneself to statistical translation techniques). Namely, there are phrase-based models and syntactic models. We had a very brief (one lecture) overview of syntactic models at the end. My beef is with phrase-based models.

The problem is that we've gone though all this prettiness to develop these word-based models, and then I have to teach them grow-diag-final, phrase extraction and phrase scoring. I almost felt embarrassed doing so. The problem is that these things are obviously so heuristic that throwing them on top of this really pretty word-for-word translation model just kills me. And it's not just me: the students were visibly upset by the lack of real modeling behind these techniques.

One option would be just not to teach this stuff. I don't really think that it sheds much light on the translation process. The reason I don't like this solution is because it's nice to be able to say that they will have a handle on a not-too-difficult to understand/implement method for doing real-world MT. Instead, I could just spend that time on syntactic models. The situation there is better (you can talk about the hierarchy of tree transducers, etc.), but not perfect (eg., all the work that goes in to rule extraction is not too dissimilar from all the work that goes into phrase extraction).

I suppose that this is just the defacto problem with a relatively immature field: there hasn't been enough time for us to really tease apart what's actually going on in these models and try to come up with some coherent story. I'd love a story that doesn't involve first doing word alignment and, is, in some sense, integrated.