An amazing book I read these days, I thought it would be a less complexity and easy reading undergo, but most surprisingly is that most of the content has close associated relationship with statistics, data mining and machine learning, especially statistics and data mining, which I know little about before, will have a seriously looking at later
Statistics is a branch of mathematics dealing with the collection, organization, analysis, interpretation and presentation of data. In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as “all people living in a country” or “every atom composing a crystal”. Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments. See glossary of probability and statistics.
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent method) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
In AI history, probabilistic ever be the domain of this area but now replaced by statistics methods, now I can say really understand what happened that time:
However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert systems had come to dominate AI, and statistics was out of favor. Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as “connectionism”, by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.
Author Dean Abbott’s video:
