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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

In the recent world, we are surrounded by various social networks. In every company, every institution, and every place worldwide, people call each other, exchange emails, text messages, post in forums, co-author documents, meet at diverse events, etc. In other words, they communicate and collaborate with each other creating and maintaining mutual relationships in a complex social network. The traditional expert finding systems try to locate a relevant expert to whom the query should be sent. However, most of the experts are not willing to solve the problems for people they do not know. In our proposed novel system, a social paradigm is used to encourage experts to send their solutions. By means of recommendations, the system propagates the query not directly to the expert, but to friends and colleagues of the expert through the acquaintance chain existing in the social network. The experts are more likely to answer if the requests come from their acquaintances. The general idea, model, and simulations on the recommender system for query propagation in the social network are presented in the paper.

https://www.ii.pwr.edu.pl/~kazienko/pub/2012/CJ2012.pdf

SocLaKE: Social Latent Knowledge Explorator

Most of the real social networks extracted from various data sources evolve and change their profile over time. For that reason, there is a great need to model evolution of networks in order to enable complex analyses of theirs dynamics. The model presented in the paper focuses on definition of differences between following network snapshots by means of Graph Differential Tuple.

https://www.ii.pwr.edu.pl/~michalski/publications/pub_2011_socinfo.pdf

Modelling social network evolution

Where should I publish? Heterogeneous, networks-based prediction of paper’s citation success

PersonalizedWeb Advertising Method..

Network motifs are small subgraphs that reflect local network topology and were shown to be useful for creating profiles that reveal several properties of the network. Analysis of three-node motifs (triads) was used in this paper to track the temporal changes in the structure of large social network derived from email communication between the employees of Wroclaw University of Technology.

Temporal Changes in Connection Patterns of an Email-Based Social Network

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