Probability Theory and Stochastic Processes

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 learn

• Definition 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.

By

SkillGems Education

PUDI V V S NARAYANA

Who this course is for:

• This subject ias helpful to Machine learning,Deep learning as well as degree and Post graduate courses in Engineering