Free Download Probability Theory And Stochastic Processes 2023Published 8/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 694.72 MB | Duration: 1h 48m
Learn the fundamental concepts on probability which is useful in the areas of machine,deep learning applications
What you'll learn
Introduction to Probability:Set Theory
Types of Events,Relative Freequency and its properties
Concept of Probability:Axioms and Theorems
Conditional and Joint probabilities and Bayes Theorem
Requirements
Mathematical Knowledge , lntegration and Differentiations required to solve some of the problems
Description
The main purpose of this course is to present an introductory and comprehensive knowledge of probability and random processes, with a strong emphasis on numerical examples.The prerequisite is elementary calculus, which is needed for multiple integrations. I have tried my level best to provide more information on probability, which is very useful to graduates, postgraduates, and those who are studying deep learning, and machine learning algorithms. They can take advantage of applying these concepts to their projects. In this course, you may learnDefinition of probability,deterministic and non deterministic random processes, and sets,definitions of probability,types of events; and relative frequency and its properties The later section deals with the types of approaches to finding the probability: axioms of probability, addition theorem,joint probability,conditional probability, multiplication, ,axioms of conditional probability,total probability,dependent events, and finally the Bayes theorem, along with the problems discussed here.This course will be updated from time to time to improve your skills in probability, stochastic processes, or random processes.If you have any doubts regarding the subject, feel free to ask and clear your doubts.The problems will help us better understand this subject.Happy learning.BySkillGems EducationPUDI V V S NARAYANA
Overview
Section 1: Introduction to probability
Lecture 1 Introduction
Lecture 2 Set theory
Lecture 3 set theory contd.,
Lecture 4 Law of sets
Lecture 5 Definitions on Probability
Lecture 6 Types of EVENTS
Lecture 7 Relative freequency and its properties in Probability
Section 2: Concept of Probability
Lecture 8 Types of Approaches to find the probability -Relative freequclassical,axiomatic
Lecture 9 Axioms of Probability
Lecture 10 Addition theorem on probability
Lecture 11 Joint Probability
Lecture 12 Conditional Probability
Lecture 13 Multiplication Theorem
Lecture 14 Axioms of Conditional probability
Lecture 15 Total probability
Lecture 16 Dependent Events
Lecture 17 Bayes theorem
Lecture 18 #problem1
Lecture 19 #problems
Lecture 20 #problem on bayes theorem
This subject ias helpful to Machine learning,Deep learning as well as degree and Post graduate courses in Engineering
Homepage
https://www.udemy.com/course/probability-theory-and-stochastic-processes/
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