Maximum Likelihood Estimation: The Engine Behind Every AI Model
Автор: Practical stats
Загружено: 2026-02-09
Просмотров: 344
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Discover the "engine under the hood" of modern science: Maximum Likelihood Estimation (MLE). Whether you are running a simple regression or training a complex Neural Network, MLE is the mathematical process used to estimate the hidden rules of our world.
In this deep dive, we bridge the gap between classical statistics and the intuition of Machine Learning. We explore why we assume data follows specific distributions and how we "tune" our parameters to match the reality we observe.
In this video, you will learn:
The Intuition of Fitting: How to find the parameters of the "tree" based on the "apples" you find on the ground.
Probability vs. Likelihood: Understanding why the data is fixed and the parameters are the variables.
The Gaussian Case Study: Visualizing how the Bell Curve shifts to find the maximum density.
The Log-Likelihood Trick: Why we transform products into sums to avoid computer "underflow."
Calculus & Optimization: How taking derivatives allows us to prove our common-sense estimators.
The Machine Learning Connection: Why training a model is essentially minimizing the Negative Log-Likelihood.
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