because this is an introductory bookwe focused on the most common machine learning tasksclassification and regression in supervised learningand clustering and signal decomposition in unsupervised learning there are many more kinds of machine learning out therewith many important applications there are two particularly important topics that we did not cover in this book the first is rankingin which we want to retrieve answers to particular queryordered by their relevance you've probably already used ranking system todaythis is how search engines operate you input search query and obtain sorted list of answersranked by how relevant they are great introduction to ranking is provided in manningraghavanand schutze' book introduction to information retrieval the second topic is recommender systemswhich provide suggestions to users based on their preferences you've probably encountered recommender systems under headings like "people you may know,"customers who bought this item also bought,or "top picks for you there is plenty of literature on the topicand if you want to dive right in you might be interested in the now classic "netflix prize challenge"in which the netflix video streaming site released large dataset of movie preferences and offered prize of $ million to the team that could provide the best recommendations another common application is prediction of time series (like stock prices)which also has whole body of literature devoted to it there are many more machine learning tasks out there--much more than we can list here--and we encourage you to seek out information from booksresearch papersand online communities to find the paradigms that best apply to your situation probabilistic modelinginferenceand probabilistic programming most machine learning packages provide predefined machine learning models that apply one particular algorithm howevermany real-world problems have particular structure thatwhen properly incorporated into the modelcan yield much betterperforming predictions oftenthe structure of particular problem can be expressed using the language of probability theory such structure commonly arises from having mathematical model of the situation for which you want to predict to understand what we mean by structured problemconsider the following example let' say you want to build mobile application that provides very detailed position estimate in an outdoor spaceto help users navigate historical site mobile phone provides many sensors to help you get precise location measurementslike the gpsaccelerometerand compass you also have an exact map of the area this problem is highly structured you know where the paths and points of interest are from your map you also have rough positions from the gpsand the accelerometer and compass in the user' device provide you with very precise relative measurements but throwing these all together into black-box machine learning system to predict positions might not be the best idea this would throw away all the information you where to go from here