k means
	How does unsupervised learning differ from supervised learning?
	What bad thing can happen when the random initial cluster centers are chosen randomly?	
	Run the algorithm by hand on data using spatial distance (x,y)
	Predict the results on data using spatial distance
	Predict the results on data using color distance (r,g,b)
	Predict the results on data using both color and spatial distance together (5 dimensions: r,g,b,x,y)
	(For example: How does the introduction of spatial (XY) distance improve clustering done on RGB only? If XY are weighted too heavily, what is the danger?)
	
Template matching
	Discuss the idea of template matching
	What are the issues with template matching?
	How would you implement template matching using a filter?

Hough transform
	What does populated voting space look like in ideal and non-ideal cases for line finder? Circle finder?
	What parameters would you use to search for a line? A Line segment? A square? A rectangle? 
	Draw the parameter space showing the votes that are cast when searching for a line, segment, square, etc.
	
PCA
	How do you compute the principal components? (eigenvectors of what matrix?)
	How do you project data onto those principal components? 
	How does one reconstruct a data point back into the original coordinate space?
	What are the principal components (visually)?
	How do you determine what dimension you can reduce your data set to?
	When one performs PCA on a series of nature scenes, the single principal component corresponds to which color axis?
	Describe how the relative sizes of the eignvalues relate to the distribution of the data in the original space.
	Novel applications of PCA, showing you understand the process.
	
	
	
Motion:
	Draw vector fields for pan/zoom/etc.
	Discuss the aperture problem. Show where it arises. Describe a way to solve it.
	Show how one can use a Taylor series to determine a motion vector (or set of motion vectors) that is consistent with a given point. Explain how we know each of the variables in the expansion (f(x,y,t), df/dx, di/dx, etc)
	How does one find interest points?
	Why is tracking interest points faster than finding them initially?
	
	
Bayesian classifiers
	Given distributions of features for two classes:
		Find the optimal threshold (the one that minimizes the error rate) and justify why it must be there.
		Draw ROC curves that arise from moving the threshold.
	Describe classification using the MAP principle
	Use the MAP principle to find the most likely class, given priors and evidence from one or more detectors.
	
Cumulative:
	Can you explain your term project or past projects in terms of the features + classifier framework I presented on the first day of class?