Recently I want to start writing a new series, this time it is not coding, but mathematics. Of course, I'm not a math major. The math foundation is actually very poor, but I'm very interested. I'm really addicted to vegetables.
It is precisely because I am very good, so the things discussed in this series are not difficult mathematics, but the basic knowledge learned in college, including probability theory and statistics, calculus, linear algebra, etc. I hope to use a re-examined Reviewing this knowledge from an angle, the focus is on understanding their essential principles and applications, rather than pushing formulas and brushing questions all day long like when studying in college.
Therefore, I will not list the basic concepts and formulas step by step like a textbook, but will find some topics that I think are more interesting, usually some theorems and formula conclusions, and discuss them. I also use as few complex mathematical formulas as possible, and instead explore how they work from an intuitive perspective.
This series starts with probability theory and statistics, which are the foundation of machine learning and the most important basic mathematics subjects in economics and finance disciplines; even from a practical point of view, it can be said that this is learned in university , the most useful mathematics course for future work and life, there is no one. Statistics does not have as many daunting formula symbols and abstract concepts as other mathematical disciplines (although there are many), and it discusses what is so close to life and intuition, yet it is sometimes very counter-intuitive, The bottom layer hides such a profound and rigorous mathematical principle; this process of establishing mathematical models and returning to the essential principles from intuition is what makes it attractive.
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Probability and Statistics
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