Parameter Estimation Lecture
Nowadays, Artificial Intelligence drives scientific and economic growth worldwide. This is largely due to advances in Machine Learning (ML). Its applications span and revolutionize almost every human activity:
-Autonomous Systems (cars, drones, vessels),
-Media Content and Art Creation (including fake data creation/detection), Social Media Analytics,
-Medical Imaging and Diagnosis,
-Financial Engineering (forecasting and analytics), Big Data Analytics,
-Broadcasting, Internet and Communications,
-Robotics/Control
-Intelligent Human-Machine Interaction, Anthropocentric (human-centered) Computing,
-Smart Cities/Buildings and Assisted living.
-Scientific Modeling and Analytics.
This lecture overviews Parameter estimation that has many applications in Statistics and Pattern Recognition. It covers the following topics in detail: Data analysis needs: Probabilistic data modeling, Estimation of pdf parameters (Location and dispersion parameters). Maximum Likelihood Parameter Estimation (ML Estimation for Gaussian Distributions, ML Estimation for Laplacian Distributions, Robustness of arithmetic mean and median). Maximum a Posteriori Probability Estimation.