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The goal of this assignment is to incentivize learning to write reasonably good python/numpy code. This is so that:

  • You don’t learn it on your own and discover some super useful function at the end of the semester
  • If you’re doing something like calculating eigenvectors wrong, you find out in a low-stakes way
  • You get credit for spending time doing this

If you need an introduction to Python or numpy, you can check out this tutorial.

I highly recommend this linear algebra review and reference. (At least for for this class) you don’t need to know:

  • determinants (you may see them once – a rotation has a determinant of 1)
  • quadratic forms and positive-definiteness
  • fun facts about eigenvectors and eigenvalues – just the Ax = lx for x != 0
  • the Hessian
  • Eigenvalues as Optimization.

Some of this is quite useful in other areas though and does appear in ML.

This assignment is due on Wednesday, February 3, 11:59pm ET.