Description
When it comes to the programming part, this assignment can be put into one sentence: take twoclass linear discriminant analysis [LDA, the classi er that assumes the classconditional distributions to be Gaussian with the same covariance matrix and its parameters are estimated through maximum likelihood] and implement two di erent ways of performing semisupervised learning for this classi er. You do need to do a bit more, however,. . . The second and more important part of the exercise is concerned with constructing/designing insightful experiments that illustrate the pros and cons of your methods.
When it comes to the implementation of your two semisupervised approaches for LDA, you are certainly allowed to take any inspiration from other works, papers, web pages, etc., you are even allowed to implement existing methods. In any case, do provide proper references to where you got your inspiration from!
Now, let us make this challenging assignment a bit more concrete. Here are the more speci c questions for you to answer and exercises for you to do.
Real

De ne and describe your two [really di erent?] ways of semisupervised learning for LDA on an algorithmic level. Keep the descriptions for the two methods clearly separate. Before giving these descriptions, do note item d. The more di erent your two choices are, the easier it will be to solve those later exercises.
b Take the MAGIC Gamma Telescope Data Set from the UCI repository^{1} and rst normalize all 10 features^{2} on the full data set once before all other experiments. Based on this normalized data set, make learning curves against the number of unlabeled samples for a total of 25 labeled samples in the training set. Check, at least, adding 0, 10, 20, 40, 80, 160, 320, and 640 unlabeled samples and see how the expected error rates change. Compare the two curve to the supervised error rates. Make sure

See https://archive.ics.uci.edu/ml/machinelearningdatabases/magic/magic04.data. Note that the last column contains the class labels, which are encoded as g and h. The rst 10 columns are the features.
^{2}That is, make all 10 feature standard deviations equal to 1.
you repeat your experiments su ciently often to get some nice and, possibly, smooth curves. Do you get signi cant changes in error rates?

With the same preprocessed data set as in b, make the same type of plots, but now plot the loglikelihood^{3} [and not the error rate] versus the number of unlabeled data.
Imaginary
d Construct two arti cial data sets. On the one data set, your rst semisupervised LDA should work well in terms of the error rate and improve over the regular supervised learner, but the second should give deteriorated performance on this same set: its performance should be worse than the supervised classi er. On the other data set, it should be the other way around: the second semisupervised LDA should work better than the supervised learner and the rst learner should fail to do so. Consider the setting in which you take few labeled samples and a large number of unlabeled samples [no need for learning curves]. Explain why the respective improvements and failures are expected.
My assessment: you should be able to keep your report within three pages.
^{3}On a test set of course. Make sure that you test on sets of the same sizes or ensure in some other way that you can compare the performance between di erent amounts of added unlabeled data.