Maximum Likelihood Estimation- An Introduction
- Intermediate level knowledge of Probability, Statistics and Calculus
The purpose of the maximum likelihood estimation (MLE) is to find or tune the parameters of the distribution in a way to explain the data OR how to learn the distribution/ model parameters from this data?
The purpose of fitting distribution to this data is to find the parameters of the distribution such that using those parameters we can extract more data of similar nature.
Maximum likelihood will look for the values of parameters that maximizes the likelihood function that is the value of parameters that says that this data is most likely to belong this distribution.
In this course, students will learn about the fundamental concept of Maximum likelihood Estimation, Parameters of Maximum Likelihood Estimation, Derivations of the Parameters, Solved Example on Maximum Likelihood Estimation. By the end of this course, students will be able to derive the parameters from the distribution. For brevity, we are only covering Gaussian distribution in this course since Gaussian is the most commonly used distribution.
Following is the breakdown of the course.
1. Events, outcomes and probability.
2. Concept of Maximum Likelihood Estimation.
3. Parameters of Maximum Likelihood Estimation.
4. Calculations of Parameters.
5. Multivariate Gaussian Distribution.
6. Derivation of Parameters.
7. Numerical Example on MLE.
8. Dealing with arrays in Python.
9. Plotting and visualization.
Who this course is for:
- Any body who loves Mathematics can take this course
- 02:45Events, outcomes and probability
- 06:21Concept of Maximum Likelihood Estimation
- 06:14Parameters for Maximum Likelihood Estimation
- 03:49Calculation of Parameters
- 06:35Multivariate Gaussian Distribution
- 11:44Derivation of Parameters
- 09:45Numerical Example on MLE
Dr. Zeeshan is PhD in Electrical and Computer Engineering from Ryerson University Toronto. He has more than 18 years of teaching and research experience. He has taught many courses related to Computer and Electrical Engineering. His research interests include Machine learning, Deep learning, Computer vision, Signal and Image processing and multimodal fusion. He has publications in reputed journals and conferences.