No pseudo-inverse, no singular value decomposition, no least squares regression, no k-nearest neighbors and I'm sure I'm leaving many other things, all fundamen

No pseudo-inverse, no singular value decomposition, no least squares regression, no k-nearest neighbors and I'm sure I'm leaving many other things, all fundamental algebra very much needed for understanding and developing of computer science (e.g. machine learning, data storage and compression), telecommunications (e.g. queueing theory, multimedia coding and streaming) and many other fields related to engineering. I lovehate maths (I really struggle with them), but I honestly think our linear algebra book back in grad school was 600 pages for a good reason, you just can't do proper engineering without it. (Also I have another really thick book on the most important numerical methods for implementing that algebra on a computer, so, come on! :-))