L21: Bayesian estimation | priors, posteriors & bayes’ theorem in parameter estimation

Welcome to Lecture 20 of the course "Machine Learning Techniques" by Prof. Arun Rajkumar. Full Course: https://study.iitm.ac.in/ds/course_pa... Video Overview This lecture explores the limitations of Maximum Likelihood Estimation (MLE) and introduces Bayesian modeling as a powerful alternative. Learn how to incorporate prior knowledge or “hunches” about parameters into the estimation process, moving from a purely data-driven approach to one that leverages both prior beliefs and observed data. We revisit the coin toss example (Bernoulli trials) to illustrate how to encode and update our beliefs using probability distributions and Bayes’ theorem. Discover how this approach can lead to potentially better estimators in many practical scenarios. About IIT Madras' online Bachelor of Science programme IIT Madras offers four-year BS programmes that aim to provide quality education to all, irrespective of age, educational background, or location. The BS programme has multiple levels, which provide flexibility to students to exit at any of these levels. Depending on the courses completed and credits earned, the learner can receive a Foundation Certificate from IITM CODE (Centre for Outreach and Digital Education), Diploma(s) from IIT Madras, or BSc/BS Degrees from IIT Madras. For more details, Visit: https://www.iitm.ac.in/academics/stud... #MaximumLikelihoodEstimation #BayesianModeling #PriorDistribution #PosteriorDistribution #BayesTheorem #CoinToss #BernoulliTrials #ParameterEstimation #DomainKnowledge #MachineLearning #Statistics #EstimationProcedure #Hunch #Belief #ProbabilityDistribution #BetaDistribution #BayesianStatistics #AppliedML #UncertaintyModeling #StatisticalLearning