Machine learning *

In 2017, the media heard a very interesting story about a startup that robotizes milking cows on industrial dairy farms. The company is called R-SEPT, and back then it received 10 million rubles of investment. But a year has passed, and there's still no news on what happened further. We contacted Aleksey Khakhunov (AlexeiHahunov), the founder of the startup, and discussed the development. It turns out that the whole year his team was getting the prototype of the robot into shape, and just a week ago they conducted their first field test on the farm.

Under the cut there's a story about a robotics student who grew up on his parents' farm, turned the University diploma into an industrial startup, as he collected the first manipulators with his friends, and then scaled up to the level of state programs for the robotization of agriculture. And the most important is how the iron hand of the robot and the machine vision are better than a living milkmaid.

When you study an abstract subject like linear algebra, you may wonder: why do you need all these vectors and matrices? How are you going to apply all this inversions, transpositions, eigenvector and eigenvalues for practical purposes?

Well, if you study linear algebra with the purpose of doing machine learning, this is the answer for you.

In brief, you can use linear algebra for machine learning on 3 different levels:

• application of a model to data;
• training the model;
• understanding how it works or why it does not work.