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Bayesian Methods: Bayesian Concepts & Core Components

Bayesian Methods: Bayesian Concepts & Core Components


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore the core concepts of Bayesian methods and the implementation of Bayes' theorem and methods in machine learning. Examine Bayesian statistics and analysis with a focus on probability distribution and prior knowledge distribution.



Expected Duration (hours)
1.0

Lesson Objectives

Bayesian Methods: Bayesian Concepts & Core Components

  • discover the key concepts covered in this course
  • describe the concept of Bayesian probability and statistical inference
  • describe the concept of Bayes' theorem and its implementation in machine learning
  • identify the role of probability and statistics in Bayesian analysis from the perspective of frequentist and subjective probability paradigms
  • describe standard probability, continuous, and discrete distribution
  • recall the essential ingredients of Bayesian statistics including prior distribution, likelihood function, and posterior inference
  • recognize the implementation of prominent Bayesian methods including inference, statistical modeling, influence of prior belief, and statistical graphics
  • identify the core concepts of Bayesian machine learning from the perspective of modeling, sampling algorithms, and variation inference
  • describe prior knowledge and compare the differences between non-informative prior distribution and informative prior distribution
  • recall the steps involved in Bayesian analysis, including modeling data, deciding prior distribution, likelihood construction, and posterior distribution
  • specify the essential ingredients of Bayesian statistics and recall the prominent Bayesian methods and the steps involved in Bayesian analysis
  • Course Number:
    it_mlbmmldj_01_enus

    Expertise Level
    Intermediate