ICWSM, semi-supervised learning

Returned from ICWSM, and was inspired to perhaps start blogging again, but we'll see how long that lasts.

The tutorial at ICWSM went well (pdf slides available at that link, ppt available by emailing me). I will be giving it again at NESCAI. There were a lot of great talks and posters at ICWSM; a lot more toward the text/sentiment mining side of things than last year, but still a great variety of concepts.

While in Seattle I missed the 10-601 class lectures on semi-supervised learning, and had to prepare a recitation anyway. So as part of that preparation I came across a good survey paper by Xiaojin Zhu. It has an entire section devoted to graph-based methods, some of which I hadn't heard of, so this was useful to me beyond giving me interesting things to talk about in recitation. It might be of use to try some of these algorithms on community detection in networks.

Teaching Seminar

I just took my second grad student teaching seminar from the Eberly Teaching Center.
Unlike my education courses I took as an education major in undergrad, these seem to be useful. Today's was on Teaching Perspectives. There were five main perspectives presented:

Transmission: "Teacher pitches content to students."
Apprenticeship: "Teacher, who is knowledgeable about the content, can serve as a model / guide students"
Developmental: "Students learn by interacting with content."
Nurturing: "Encourage learning by forging relationship between student and teacher."
Social Reform: "Focus on ideals, everything else is bonus.

We took a Seventeen-magazine-style questionnaire that showed how we "scored" on each perspective with respect to our beliefs, actions, and intentions. I scored high on the first three, and low on the second two, while I would have expected myself to score lower on transmission and apprenticeship than I did.

However, I think the perspective depends on the class. I'm currently TAing for an undergrad/Master's level course in Machine Learning. While I think that social reform and emotional growth are important for young adults, I don't think that's my job. These students are paying a ton of tuition money, and here they're paying to be taught about machine learning. I'm much better equipped to give them their tuition's worth in cold, hard knowledge than in a great teacher-student relationship. They're perfectly capable of getting qualitative ideas from their philosophy courses and extracurriculars, and their nurturing from their friends and other relationships. However, if I were teaching, say, a course on statistical bullshit detection, I'd have a high priority on social reform, and if I were teaching a freshman class on literature or composition, I might want to incorporate more nurturing into the class.

Anyway, it's at least something to think about while I'm teaching. And I'll probably go to more of these seminars.