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September 24, 2010
Learning how to teach
This semester has turned out to be a lot busier than I expected. Teaching, omg, takes a lot of time. At least, I've been putting a lot of time into it, developing the lectures, writing out my notes in a coherent format, developing the problem sets, refreshing my own understanding of technical topics, translating research-y material into a more instructional level, grading problem sets, meeting with students, etc. etc. etc.
This is, after all, what I signed up for as a professor. I'm also happy to say that I'm enjoying it a great deal. The students are engaging, ask interesting questions, and have already taught me new things. I do wish I had more time for research, but I suppose that almost goes without saying. One topic I expect to continue to struggle with is deciding how much material to cover and how deeply to go into it. Striking a good balance, making assignments and lectures challenging and interesting but not unreasonable or trivial, seems like an art. A good friend of mine warned me months ago that I should not underestimate how enormous a burden it would be to be completely and wholly responsible for an entire semester's material. I don't think I did underestimate it, but for sure I didn't understand, viscerally, what he meant until now.
For those of you interested in the course, here's a current list of my lecture notes and the topics I've covered. I'll update this entry with the rest of the lectures, once they're up on the course webpage.
CSCI 7000-003 Inference, Models and Simulation for Complex Systems
Lecture 1: The Poisson process, the exponential distribution and a brief introduction to maximum likelihood
Lecture 2: Mathematics of power-law distributions and power-law tails
Lecture 3: Power-law distributions in empirical data
Lecture 4: Model plausibility, hypothesis tests, model comparison and a fallacy
Lecture 5: Models of time series and simulations of random walks
Lecture 6: Random walks in empirical data
Lecture 7: Structural measures of networks: degrees, reciprocity, transitivity, similarity
Lecture 8: Structural measures of networks: distances, diameters, centrality, homophily
Lecture 9: Random graph models, degree distributions, giant components
Lecture 10: Preferential attachment, citation networks
Lecture 11: Large-scale structure, modules, communities and three ill omens
Lecture 12: Hierarchical structure, predicting missing links
Lecture 13: Macroevolution, deep time, and the evolution of species body sizes
Lecture 14: Macroevolution of whales, morphological disparity
posted September 24, 2010 02:06 PM in Teaching | permalink