Introduction to Machine learning
Posted: Feb 20, 2020
Over the past two decades machine learning has become one of the mainstays of information technology and with that, a rather central, albeit usually hidden, part of our life. with the ever increasing amounts of data becoming available there is good reason to believe that smart data analysis will become even more pervasive as a necessary ingredient for technological progress. The purpose of this chapter is to provide the reader with an overview over the vast range of applications which have at their heart a Microsoft Azure Training problem and to bring some degree of order to the zoo of problems. After that, we will discuss some basic tools from statistics and probability theory, since they form the language in which many machine learning problems must be phrased to become amenable to solving. Finally, we will outline a set of fairly basic yet effective algorithms to solve an important problem, namely that of classification. more sophisticated tools, a discussion of more general problems and a detailed analysis will follow in later parts of the book.
1.1 a taste of machine learning
Machine learning can appear in many guises. We now discuss a number of applications, the types of data they deal with, and finally, we formalize the problems in a somewhat more stylized fashion. The latter is key if we want to avoid reinventing the wheel for every new application. Instead, much of the art of machine learning is to reduce a range of fairly disparate problems to a set of fairly narrow prototypes. Much of the science of machine learning is then to solve those problems and provide good guarantees for the solutions.
Most readers will be familiar with the concept of web page ranking. That is, the process of submitting a query to a search engine, which then finds webpages relevant to the query and which returns them in their order of relevance. See e.g. figure 1.1 for an example of the query results for "machine learning". That is, the search engine returns a sorted list of webpages given a query. To achieve this goal, a search engine needs to ‘know’ which pages are relevant and which pages match the query. Such knowledge can be gained from several sources: the link structure of webpages, their content, the frequency with which users will follow the suggested links in a query, or from examples of queries in combination with manually ranked webpages. Increasingly machine learning rather than guesswork and clever engineering is used to automate the process of designing a good search engine
Problem 1.1 (eyewitness) assume that an eyewitness is 90% certain that a given person committed a crime in a bar. Moreover, assume that there were 50 people in the restaurant at the time of the crime. What is the posterior probability of the person actually having committed the crime?
(DNA test) assume the police have a DNA library of 10 million records. Moreover, assume that the false recognition probability is below 0.00001% per record. Suppose a match is found after a database search for an individual. What are the chances that the identification is correct? You can assume that the total machine learning training population is 100 million people. Hint: compute the probability of no match occurring first.
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