Read/skim “Strong and Weak Emergence” by David Chalmers from 2006 (PDF; full citation). Answer these questions:
- Define what Chalmers means by weak emergence.
- Chalmers writes, “We can think of strongly emergent phenomena as being systematically determined by low-level facts without being deducible from those facts.” Give an example (1-2 sentences) that may possibly satisfy this definition of strong emergence.
- Are the NetLogo models we looked at (sheep and wolves, ants, termites) examples of strong or weak emergence? Provide a 1-2 sentence argument.
Execute the k-means algorithm by hand on the following data:
| item # | w | x | y | z | true label |
|---|---|---|---|---|---|
| 1 | 1.0 | 1.0 | 0.0 | 1.0 | A |
| 2 | 0.0 | 0.0 | 1.0 | 0.0 | B |
| 3 | 2.0 | 2.0 | 0.0 | 2.0 | A |
| 4 | 0.0 | 0.0 | 1.0 | 1.0 | B |
| 5 | 2.0 | 2.0 | 0.0 | 2.0 | A |
| 6 | 0.0 | 2.0 | 1.0 | 1.0 | B |
Use
Run the k-means algorithm in Weka using this dataset: iris.arff (iris species clustering).
Choose
Execute the k-nearest neighbor algorithm by hand on the dataset below
(same as before). Use
| item # | w | x | y | z | true label |
|---|---|---|---|---|---|
| 1 | 1.0 | 1.0 | 0.0 | 1.0 | A |
| 2 | 0.0 | 0.0 | 1.0 | 0.0 | B |
| 3 | 2.0 | 2.0 | 0.0 | 2.0 | A |
| 4 | 0.0 | 0.0 | 1.0 | 1.0 | B |
| 5 | 2.0 | 2.0 | 0.0 | 2.0 | A |
| 6 | 0.0 | 2.0 | 1.0 | 1.0 | B |
Run the k-nearest neighbor in Weka using this dataset: letter.arff
(handwritten letter classification). Find a good value of
Explain the differences between k-means and k-nearest neighbor algorithms.
Play around with Weka. Report how well at least three different classification algorithms (avoid k-means and k-nn) perform on the letter.arff data with 10-fold cross validation. Collect accuracies in a table.