 Hello everyone, this is Professor Manjesh Kumar Hanewal from the Department of Industry Engineering and Operations Research. So this course is about understanding various tools and techniques available to model stochastic scenario that we face in life. For example, when you want to go from one place to another place, your travel time could depend on many factors like how much is the traffic there, what is the weather condition etc. So in this case, if you want to estimate or if you want to compute on an average how much the time it takes for you to reach the destination, how you will go about this, what kind of tools and techniques are available to you. Another example is suppose you have developed a system that is going to work in different different conditions and you want to compute what is the expected lifetime that it is going to survive for then how you will go about this when that the different situation that it is going to face is stochastic in nature. So to understand this, we will start with by studying the basics of probability distributions, then what is the random variable and what kind of frequently used distribution to model various scenarios we face in life and other basic things like what is conditional distribution, what is joint distribution and we as we graduate, we will try to understand when we have a sequence of random variables to deal with which we call it as stochastic processes. So as we move on in the second half of the course, we will understand different notions of convergence of stochastic process and we particularly focus on a special type of random process called Markov chains. Markov chains are very popular and they come very handy to model various scenarios we face in life. So overall this course is about how to model a stochastic system that we are faced with using in in a very structured way and get some insights into the behavior of the system. Thank you very much. We I hope you will learn a lot from this course.