Running A Machine Learning Summer School

We just finished the Chicago 2005 Machine Learning Summer School. The school was 2 weeks long with about 130 (or 140 counting the speakers) participants. For perspective, this is perhaps the largest graduate level machine learning class I am aware of anywhere and anytime (previous MLSSs have been close). Overall, it seemed to go well, although the students are the real authority on this. For those who missed it, DVDs will be available from our Slovenian friends. Email Mrs Spela Sitar of the Jozsef Stefan Institute for details.

The following are some notes for future planning and those interested.
Good Decisions

1. Acquiring the larger-than-necessary “Assembly Hall” at International House. Our attendance came in well above our expectations, so this was a critical early decision that made a huge difference.
2. The invited speakers were key. They made a huge difference in the quality of the content.
3. Delegating early and often was important. One key difficulty here is gauging how much a volunteer can (or should) do. Many people are willing to help a little, so breaking things down into small chunks is important.

Unclear Decisions

1. Timing (May 16-27, 2005): We wanted to take advantage of the special emphasis on learning quarter here. We also wanted to run the summer school in the summer. These goals did not have a good solution. By starting as late as possible in the quarter, we were in the “summer” for universities on a semester schedule but not those on a quarter schedule. Thus, we traded some students and scheduling conflicts at University of chicago for the advantages of the learning quarter.
2. Location (Hyde Park, Chicago):
   Advantages:
   1. Easy to fly to.
   2. Easy to get funding. (TTI and Uchicago were both significant contributors.)
   3. Easy (on-site) organization.

   Disadvantages:

   1. US visas were too slow or rejected 7+ students.
   2. Location in Chicago implied many locals drifted in and out.
   3. The Hyde Park area lacks real hotels, creating housing difficulties.
3. Workshop colocation: We colocated with two workshops. The advantage of this is more content. The disadvantage was that it forced talks to start relatively early. This meant that attendance at the start of the first lecture was relatively low (60-or-so), ramping up through the morning. Although some students benefitted from the workshop talks, most appeared to gain much more from the summer school.

Things to do Differently Next Time

1. Delegate harder and better. Doing various things rather than delegating means you feel like you are “doing your part”, but it also means that you are distracted and do not see other things which need to be done….and they simply don’t get done unless you see it.
2. Have a ‘sorting session’. With 100+ people in the room, it is difficult to meet people of similar interests. This should be explicitly aided. One good suggestion is “have a poster session for any attendees”. Sorting based on other dimensions might also be helpful. The wiki helped here for social events.
3. Torture the speakers more. Presenting an excess of content in a minimum of time to an audience of diverse backgrounds is extremely difficult. This difficulty can not be avoided, but it can be ameliorated. Having presentation slides and suggested reading well in advance helps. The bad news here is that it is very difficult to get speakers to make materials available in advance. They naturally want to tweak slides at the last minute and include the newest cool discoveries.
4. Schedules posted at the entrance.

The Future There will almost certainly be future machine learning summer schools in the series and otherwise. My impression is that the support due to being “in series” is not critical to success, but it is considerable. For those interested, running one “in series” starts with a proposal consisting of {organizers,time/location,proposed speakers,budget} sent to Alex Smola and Bernhard Schoelkopf. I am sure they are busy, so conciseness is essential.