Free Online Course on Computational Linear Algebra

Computational Linear Algebra is the method by which computers are made to perform using matrices of data science. Why is it necessary to learn it? Because it is the way forward in data science.

What is Computational Linear Algebra exactly?

Computational Linear Algebra is manipulating matrices since almost all data can be represented as a matrix: time-series, structured data, anything that fits in a spreadsheet or SQL database, images, and language.

Free Online Course on Computational Linear Algebra

What are the benefits of learning Computational Linear Algebra?

It makes the system faster
Increases accuracy of computation
Efficiency in retaining memory
Improves scalability

Practical aids to learn

Randomized algorithms
Numba: a library that compiles Python to optimized C code
PyTorch: an alternative to Numpy that runs on the GPU (also, an excellent deep learning framework, although we are using it just as a Numpy alternative here to speed up our code)

Teaching method

This course uses the same top down, code first, application centered teaching method

Course materials

The primary resource for this course is the free online textbook of Jupyter Notebooks, available on Github.
Playlist of lecture videos

Who is conducting this?

Fast.ai

When

Course material will be given online and candidates can learn at their own pace

Practical applications of Computational Linear Algebra

How to identify the foreground in a surveillance video, by decomposing a matrix into the sum of two other matrices
How to reconstruct an image from a CT scan using the angles of the x-rays and the readings
How to identify topics in a corpus of documents
The algorithm behind Google's PageRank, used to rank the relative importance of different web pages