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

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

In this chapter, we discuss the agency theory. The agency theory is a principle utilized in an attempt to explain the complicated relationship that exists between the owners (principal) and managers (agents) of the business. Based on this, we propose that the agency theory is an attempt to explain the complexity of human behaviour in the principal-agent relationship. We are of the view that when the desires or goals of the principal and agent conflict, it is difficult or expensive for the principal to verify what the agent is doing. Since the theory is an attempt to explain the complexity of human behaviour in the principal-agent relationship, we pose the question: what happens to the theory in the era dominated by intelligent machines? We conclude that with AI, there will be new frontiers. These new frontiers include, among other things, the certainty of sharing of intelligent information that would, in the context of the agency theory, be available to both the agent and the principal. Further, we observe that the advantages of intelligent systems are updateability and connectivity. Using these strengths, we think intelligent agents will be swift in picking up the information discrepancies. Intelligent systems have the capability of harvesting information from different sources. Once gathered, this information will be updated in the principal’s system, given that the systems will be integrated. Finally, we think it conceivable that perhaps the agent, knowing that intelligent agents are deployed widely and that these intelligent systems have the capability of harvesting data from different repositories, will moderate his or her behaviour to be closely aligned to that of the principal.

The Agency Theory

Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process. Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include: multi-layer perceptron neural networks for real-time FEM updating; particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models; simulated annealing to put the methodologies into a sound statistical basis; and response surface methods and expectation maximization algorithms to demonstrate how FEM updating can be performed in a cost-effective manner; and to help manage computational complexity. Based on these methods, the most appropriate updated FEM is selected using the Bayesian approach, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements systematically through the formulations of prior distributions. Throughout the text, case studies, specifically designed to demonstrate the special principles are included. These serve to test the viability of the new approaches in FEM updating. Finite Element Model Updating Using Computational Intelligence Techniques analyses the state of the art in FEM updating critically and based on these findings, identifies new research directions, making it of interest to researchers in strucural dynamics and practising engineers using FEMs. Graduate students of mechanical, aerospace and civil engineering will also find the text instructive.

Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics

Missing data creates various problems in analysing and processing data in databases. In this paper we introduce a new method aimed at approximating missing data in a database using a combination of genetic algorithms and neural networks. The proposed method uses genetic algorithm to minimise an error function derived from an auto-associative neural network. Multi-layer perceptron (MLP) and radial basis function (RBF) networks are employed to train the neural networks. Our focus also lies on the investigation of using the proposed method in accurately predicting missing data as the number of missing cases within a single record increases. It is observed that there is no significant reduction in accuracy of results as the number of missing cases in a single record increases. It is also found that results obtained using RBF are superior to MLP.

/pdf/the-use-of-genetic-algorithms-and-neural-networks-to-8n34rb5w88.pdf

The use of genetic algorithms and neural networks to approximate missing data in database

The Use of Genetic Algorithms and Neural Networks to Approximate Missing Data in Database.

In recent years, the issue of missing data imputation has been extensively explored in information engineering. Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques presents methods and technologies in estimation of missing values given the observed data. Providing a defining body of research valuable to those involved in the field of study, this book covers techniques such as radial basis functions, support vector machines, and principal component analysis.

Tshilidzi Marwala

Papers

The Agency Theory

Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics

The use of genetic algorithms and neural networks to approximate missing data in database

The Use of Genetic Algorithms and Neural Networks to Approximate Missing Data in Database.

Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques