Hello everyone and welcome to this course on an introduction to machine learning in this course we will have a quick introduction to machine learning and this will not be very deep in a mathematical sense but it will have some amount of mathematical trigger and what we will be doing in this course is covering different paradigms of machine learning and with special emphasis on classification and regression tasks and also will introduce you to various other machine learning paradigms. In this introductory lecture set of lectures I will give a very quick overview of the different kinds of machine learning paradigms and therefore I call this lectures machine learning. ) A brief introduction with emphasis on brief right, so the rest of the course would be a more elongated introduction to machine learning right. So what is machine learning so I will start off with a canonical definition put out by Tom Mitchell in 97 and so a machine or an agent I deliberately leave the beginning undefined because you could also apply this to non machines like biological agents so an agent is said to learn from experience with respect to some class of tasks right and the performance measure P if the learners performance tasks in the class as measured by P improves with experience. So what we get from this first thing is we have to define learning with respect to a specific class of tasks right it could be answering exams in a particular subject right or it could be diagnosing patients of a specific illness right. So but we have to be very careful about defining the set of tasks on which we are going to define this learning right, and the second thing we need is of a performance measure P right so in the absence of a performance measure P you would start to make vague statement like oh I think something is happening right that seems to be a change and something learned is there is some learning going on and stuff like that. So if you want to be clearer about measuring whether learning is happening or not you first need to define some kind of performance criteria right. So for example if you talk about answering questions in an exam your performance criterion could very well be the number of marks that you get or if you talk about diagnosing illness then your performance measure would be the number of patients that you say are the number of patients who did not have adverse reaction to the drugs you gave them there could be variety of ways of defining performance measures depending on what you are looking for right and the third important component here is experience right.