Course details

Bayesian Methods: Bayesian Concepts & Core Components

Bayesian Methods: Bayesian Concepts & Core Components


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This 11-video course explores the machine learning concepts of Bayesian methods and the implementation of Bayes' theorem and methods in machine learning. Learners can examine Bayesian statistics and analysis with a focus on probability distribution and prior knowledge distribution. Begin with a look at the concept of Bayesian probability and statistical inference, then move on to the concept of Bayesian theorem and its implementation in machine learning. Next, learn about the role of probability and statistics in Bayesian analysis from the perspective of frequentist probability and subjective probability paradigms. You will examine standard probability, continuous distribution, and discrete distribution, and recall the essential elements 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. Describe prior knowledge and compare the differences between non-informative prior distribution and informative prior distribution. The steps involved in Bayesian analysis, including modeling data, deciding prior distribution, likelihood construction, and posterior distribution are also covered. The concluding exercise focuses on Bayesian statistics and analysis.



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