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

Data Mining Practical Machine Learning Tools and Techniques

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

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

/pdf/evolutionary-algorithms-for-solving-multi-objective-problems-2txxk4mqci.pdf

Evolutionary algorithms for solving multi-objective problems

In this paper is presented an Intelligent Well Systems for the Industrial Production of Oil. Such scheme is tested for gas lift (GL) oil wells. The proposal is based on the production assessment, in the utilization of the process variables (specifically, the bottom-well and surfaces pressures), and the operational scenarios detection (in the case study, the production of the oil well), with the objective of optimizing the producing performance of the well. The proposal combines intelligent techniques (Fuzzy Classification Systems) and Mass Energy Balance. Our approach allows determining and controlling the oil or gas flow that a well can produce, taking into account the completion geometry and the reservoir potential, as well as the criteria related to the well's performance curves of oil and gas.

Intelligent Well Systems

Abstract In this work, we aim to analyze the sentiments of learning resources from their textual contents. This work proposes a method for automatic assignment of emotional state to learning resources, based on their feature similarity with previously labeled learning resources. Then, various feature extraction strategies, which describe the relevant information in the texts, are compared for the task of sentiments analysis, considering the two main dimensions of emotions: arousal and valence. The results are very promising, showing a very high value in the performance metrics, like the $$R^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> score. 

/pdf/sentiment-analysis-in-learning-resources-29swubx9.pdf

Sentiment analysis in learning resources

The oil well instrumentation generates a set of process variables, which must analyzed by the experts in order to determine the well state. That implicates a highly cognition task where the information generated is very important for maintenance tasks, production control, etc. In other way, the natural energy of an oil field can not be enough to lift the fluids. In these case is necessary to use another procedure to lift the oil, for example gas. That is an interesting case to be modeled by an artificial intelligence technique. Particularly, in this paper we propose an Artificial Immune System for fault detection in gas lift oil well. Our novel approach inspired by the Immune System allows the application of a pattern recognition model to perform fault detection. A significant feature of our approach is its ability to dynamically learning the fluid patterns of the 'self' and predicting new patterns of the 'non-self'

/pdf/an-artificial-immune-system-for-fault-detection-6mf3k5p52d.pdf

An artificial immune system for fault detection

The Department of Computer Science at the University of Los Andes has proposed a new curriculum model based on the profile defined by ACM/IEEE, and a pedagogical model based on the "learning by doing" paradigm, in order to ensure a process of self-formation of the student. The knowledge management and the learning process are based on the concept of clouds. The new educational model is based on three clouds: a) the self-formation cloud which is composed of everything related to the student's education (curriculum, etc.); b) the learning paradigm cloud which is related to the paradigms, strategies, forms of assessment, and learning tools; and c) the knowledge sources cloud which is composed of all existing knowledge in the world, in all its forms, from all possible sources. To give support to the educational model, a web platform based on Service Oriented Architecture (SOA) is proposed in this work. The system consists of three management layers; each one manages each one of the clouds described above. The implementation of each layer is made as web services. Each layer has its own data structure defined like ontology. This is called Extended Database, which also has the ability to support processes of reasoning over it.

Computational platform for the educational model based on the cloud paradigm

In this article a Dynamic Semantic Ontological Framework (MODS) is proposed for web semantics to permit the interpretation and formalizing a query carried out by a user in natural language. As such, MODS permit user queries in natural language. To do so, MOD transforms the query in a format that represents the meaning of the query (RSC) utilizing their different components: lexicon, linguistic task ontology, and the ontology of the domain. This way, MODS use mechanisms of ontological semantics and tools for processing natural language for processing the user's query. The RSC is transformed in an ontological language (OWL) to later be used by the web semantics. In this work, the MOD architecture is presented.

Dynamic semantics ontological framework for web semantics

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