AI engineering glossary · Probability
What is MLE (maximum likelihood estimation)?
Maximum likelihood estimation (also called MLE): A method of choosing model parameters as the values that make the observed data most probable, found by maximising the likelihood function or its logarithm. Many machine learning losses, such as cross-entropy, are derived from it.
Maximum likelihood estimation: an example
For 7 heads in 10 tosses, the value of p that maximises likelihood is 7 / 10 = 0.7.
Related terms
More Probability terms
- Median
- Memoryless property
- Mode
- Monte Carlo simulation
- Monty Hall problem
- Multiplication rule
- Mutually exclusive events
- Normal distribution
Where you learn it
Maximum likelihood estimation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.