Probability Theory And Stochastic Processes

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