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W9_L9: Parameter estimation: properties of estimators
L19: Introduction to estimation | probabilistic models, parameter estimation
W9_L2: Parameter estimation: introduction to parameter estimation
Pillai: MAP Estimator and Bayesian Inference
Pillai: Principle of Maximum Likelihood (Fisher)
Lecture 58: Parameter Estimation
Lec 11: Parameter Estimation and Maximum Likelihood Estimation
W9_L3: Parameter estimation: error in estimation
Pillai Probability The Best Linear Estimator (1/2)
Week-9 SWU : Parameter Estimation
Pillai: Maximum Likelihood (ML) Estimator with Examples
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Last Updated: October 21, 2026
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Summary
This video is based on Lecture by Prof. Welcome to Week 9 Lecture 8 of the course "Statistics for Data Science - II" by Prof. Andrew Thangaraj Full Course: ... Maximum a-posteriori (MAP) and Minimum Mean Square Error (MMSE) Welcome to Lecture 18 of the course "Machine Learning Techniques" by Prof. Arun Rajkumar. Full Course: ... Principle of Maximum Likelihood (ML) is universally used to find the best ... the data because we assume theta is a Machine Learning and Deep Learning - Fundamentals and Applications onlinecourses.nptel.ac.in/noc23_ee87/preview ... Priinciple of Maximum Likelihood (ML)