Probability Theory and Stochastic Processes
Learn the fundamental concepts on probability which is useful in the areas of machine,deep learning applications
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
What You Will 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
Who Should Attend!
- This subject ias helpful to Machine learning,Deep learning as well as degree and Post graduate courses in Engineering