Changing Times

An old friend from college sent me an e-mail, and it got me thinking.  When I was an undergraduate at Harvard some significant number of years ago, I took the graduate level algorithms course offered by Michael Rabin and the graduate level complexity course by Les Valiant.  There were maybe a half dozen people in each of the classes.  (They were great classes, of course. But CS at Harvard back then was really, really small.) 

This semester, I'm teaching the graduate level course on randomized algorithms and probabilistic analysis.  Right now, the enrollment is 74 students;  well more than half are undergraduates.  Somehow, that says something to me -- about how the field has grown, and in at least some regards how Harvard has changed.  And about how much more prepared students are these days for these kinds of classes.  (Knowledge or probability is much more prevalent.)  Class sizes have been creeping up for so long that while it's noticeable year-to-year, it's much more stark and remarkable when I think back to my own time in college. 

Of course, it's also on my mind because it's a pain teaching a graduate class that large.  But it's a pain I can live with -- if I didn't like teaching, I wouldn't have become a professor.  And it's gratifying, if not a little bit shocking, that there's this kind of interest in the subject I really love, that I've been excited by for decades. 

Off to ALGO/ESA 2019

I'm shortly hopping on a plane to head to ALGO/ESA.  I'll be giving a survey-ish talk on Learning Augmented Algorithms, covering my work so far in the area as well as some of the work by others.  I think it's a highly promising direction fitting in the framework of Beyond Worst Case Analysis, so I'm excited to give the talk, and hoping it's still a novel enough area to be new to most of the audience. 

For those of you who are there, feel free to say hi -- I'm looking forward to talking to people. 

Happy New Academic Year: Teaching Randomized Algorithms

It seems I haven't written on this blog for a while.

Today was the start of a new semester.  I'll be teaching Randomized Algorithms and Probabilistic Analysis, using the new edition of my book with Eli Upfal as a base, and throwing in other material.  (Everyone should buy the book!  Here's a link.) 

It's a graduate level class, but generally designed for first year graduate students, and there were a lot of undergrads "shopping" it today.  (We don't do pre-registration at Harvard, and students get the first week to choose classes, known as shopping.)  So many that people were standing out the doors of the room.  But because we have a bit of a shortage of classes this semester, I'm guessing there's a good fraction of students just checking it out.  We'll see Thursday, but for now I'll predict we'll fit in the classroom, and wait to see if I'm wrong.  (If I'm wrong, that's wonderful too.)   

It's been four years since I last taught the course, so this time I'm trying something new.  When I've previously taught the course, I tried to make the class inviting and friendly by telling the class we'd begin without assuming the class knew probability, and so the first couple of weeks would be reviewing basics (like, say, linearity of expectations and union bounds), albeit in a CS algorithms context.  This time, I let the class know I'm assuming they know (or will pick up) basic probability, and so they should read chapters 1-4 on their own, and we'll start with Chapter 5, Balls and Bins models.  Over the last decade, I've seen a huge shift in probability knowledge -- Stat 110, Harvard's probability course, has become one of Harvard's biggest classes.  Many students have already taking AI or ML or even data science courses where they've done some (further) probability.  It feels appropriate (and safe) to assume people entering in the class know probability, or can review what they need on their own, and start the class further along.

Now finally, a request.  It's actually hard for me to teach when using this book, because I don't want to just read the book to the students.  That's boring.  On the other hand, if I thought something was important, I most likely already put it in the book.  We have to mix up the standard lecturing format a bit.  So two things we'll be doing are

1)  doing some "puzzle problems" at the beginning of most classes, so people can try to solve problems.  (Kind of a flipped classroom approach, but not a full commitment.)
2)  reading papers, related to the class topics.

So if you have any good suggestions of probability puzzle problems, or readable papers (particularly application papers) that use relatively basic probabilistic analysis in neat ways, send them over.  I've got a semester to fill.

For curious people, here's one of today's starting problems, which I first learned about in graduate school.  (I'm pretty sure I owe thanks to Claire Kenyon for teaching it.  I'll link to the corresponding Wikipedia page on the problem maybe later.)

After lunch, Bob suggests the following game to see who pays.  Alice and Bob will each choose a different sequence of three flips.  (So they could choose "Heads-Tails-Heads'', or "Tails-Tails-Tails'' for example.)  After they choose, a fair coin will be tossed until one of their sequences appears as a consecutive subsequence of the coin tosses.  The player whose sequence appears first wins. (Note that if they choose the above sequences, and if the flips come up Heads-Tails-Tails-Tails, the player that chose Tails-Tails-Tails would win as soon as their subsequence appears;  it's not three flips, then start over again.)  Bob politely says that Alice can choose first, and after she chooses and tells him her sequence he'll choose a different sequence.  What should Alice choose?