Computational Linear Algebra for Coders
Other,, Summer 2017 , Prof. Rachel Thomas
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Updated On 02 Feb, 19
This course is focused on the question: How do we do matrix computations with acceptable speed and acceptable accuracy? The course is taught in Python with Jupyter Notebooks, using libraries such as scikit-learn and numpy for most lessons, as well as numba and pytorch in a few lessons.
4.1 ( 11 )
Predicting health outcomes on a diabetes data set with least squares linear regression:
- Linear regression in sklearn
- Polynomial Features
- Speeding up with Numba
- Regularization and Noise
How to implement linear regression yourself:
- How did Scikit Learn do it?
- Naive solution
- Normal equations and Cholesky factorization
- QR factorization
- Timing Comparison
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
March 29, 2019
Great course. Thank you very much.