introduction to machine learning and deep learning consider an abstract problem domain where we have data of the form , , xn ,yn where rn and + we do not have access to all such data but only subset using sour task is to generate computational procedure that implements the function such that we can use to make predictions over unseen data (xiyis that are correctf(xiyi let' denote as the set of unseen data--that is(xiyis and (xiyiu we measure performance over this task as the error over unseen data du xi yi   xi yi  we now have precise definition of the taskwhich is to categorize data into one of two categories ( + based on some seen data by generating we measure performance (and improvement in performanceusing the error efduover unseen data the size of the seen data |sis the conceptual equivalent of experience in this contextwe want to develop algorithms that generate such functions (which are commonly referred to as modelin generalthe field of machine learning studies the development of such algorithms that produce models that make predictions over unseen data for suchandother formal tasks (we introduce multiple such tasks later in the note that the is commonly referred to as the input/input variable and is referred to as the output/output variable as with any other discipline in computer sciencethe computational characteristics of such algorithms are an important facethoweverin addition to thatwe also would like to have model that achieves lower error efduwith as small |sas possible let' now relate this abstract but precise definition to real-world problem so that our abstractions are grounded suppose that an ecommerce website wants to customize its landing page for registered