Rules
• Use basic Python, numpy and matplotlib modules. Any other modules need my approval.
• Produce a LATEX-generated PDF of your report.
• Ask plenty of questions to ensure you have a good understanding of the project.
• The code (and reports) should look vastly different for different groups. Very similar code will incur a
hefty penalty.
• Everyone should participate…no excuses, no exceptions.
Part 1
1. Generate 20 points of X ∼ N (

−3
4

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, I) and Y ∼ N (

3
−2

, 2I). Store this dataset in a file.
2. Using Fisher’s Linear Discriminant Analysis, find the decision boundary.
3. Plot X,Y and the decision boundary. Make sure that you use a good plotting technique so that it is
easy to distinguish which datapoint is X and which is Y .
4. Calculate the accuracy of your linear classifier.
Part 2
In this part we will investigate the effects of mean for jointly Gaussian random variables on accuracy.
1. Generate 20 points of X ∼ N (

−3 + µ1
4 + µ1

, I) and Y ∼ N (

3 + µ2
−2 + µ2

, 2I).
2. Using Fisher’s Linear Discriminant Analysis, calculate the decision boundary and plot accuracy vs
µ1 ∈ [0, 3] and µ2 ∈ [0, 3]. Note that this is a 3D plot.
Part 3
1. Generate 20 points of X ∼ N (

−3
4

, Σ1) and Y ∼ N (

3
−2

, Σ2), where Σ1 =

σ1 0
0 σ1

and Σ2 =

σ2 0
0 σ2

.
2. Using Fisher’s Linear Discriminant Analysis, calculate the decision boundary and plot accuracy vs
σ1 ∈ [1, 5] and σ2 ∈ [1, 5]. Note, as in the previous part this is a 3D plot.

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