From the course: Machine Learning Foundations: Probability
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Central limit theorem - Python Tutorial
From the course: Machine Learning Foundations: Probability
Central limit theorem
- [Instructor] Central limit theorem plays an important role in ML. For example, in education, we could use it to estimate student performance on standardized tests by taking a random samples of students and testing them. By applying the central limit theorem, teachers could improve curriculum and instruction. Another example would be quality control in manufacturing to make sure that the production line output satisfies quality standards. The manufacturing manager may randomly select 100 products each day and check how many are defective. Or in finance, it can be used to estimate the distribution of investment returns. Investors can make random samples of historical returns, and by using the central limit theorem, make investment decisions. The theorem tells us that: As the size of the sample increases, the distribution of the mean across multiple samples will approximate a Gaussian distribution. Wait, what? Don't…
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