Our graduates have prepared lists of basic terms that should be studied (or remembered) before diving into the world of Machine Learning.

1️⃣ Basic concepts

* Limit and derivative

* The geometric meaning of the derivative

* Operations in vector spaces

* Matrix operations

* Systems of linear equations

* Gradient, gradient application, gradient descent

* Probability theory and statistics:

— Random variable and probability

— Mathematical expectation and variance (discrete and continuous cases)

— Standard deviation

2 . Useful concepts

* Derivative of a complex function

* Finding the extreme

* Second derivative and convexity

* Linear independence

* Rank and determinant

* Confidence intervals

It is ideal to know everything, otherwise a number of statements in the derivation of ML algorithms will need to be taken (as axioms) on faith.

Action items

1️⃣ Save the checklist for preparation

2️⃣ Like us to motivate the preparation of the list of references 📚

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