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 - Concepts and Techniques.

Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision. There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black box’ machine learning model arrives at a specific decision. It can be argued that instead efforts should be directed at building inherently interpretable models in the first place, in particular where they are applied in applications that directly affect human lives, such as in healthcare and criminal justice.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

An Introduction to MultiAgent Systems.

Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS-SVM for Unsupervised Learning LS-SVM for Recurrent Networks and Control.

/pdf/least-squares-support-vector-machines-2mmqf1mu71.pdf

Least Squares Support Vector Machines

https://lirias.kuleuven.be/bitstream/123456789/102027/1/OR_9908.pdf

Verification and validation of knowledge-based systems with an example from site selection

J. V ANTHIENEN M. SNOECK Katholieke Universiteit Leuven Department of Applied Economic Sciences Dekenstraat 2, 3000 Leuven (Belgium) Due to its ability to check a decision specification for completeness, consistency and correctness, the decision table technique has always been recognized as a very inviting formalism. Also, the decision table is not an isolated technique and shows a lot of interfaces to other representation formalisms. Few attention, however, has been paid to the modelling process itself and the introduction of computer supported modelling and interfacing of decision tables. In this paper it is shown how a decision table engineering workbench will create a significant added value to the decision table technique and address these issues of decision table modelling and interfacing.

https://lirias.kuleuven.be/bitstream/123456789/499856/1/OR_9223.pdf

Enhanced Decision Modelling through a Decision Table Engineering Workbench

Decision tables: an overview of the existing literature

This paper provides a snapshot of the state-of-the-art in information retrieval and information extraction from text through a selection of commercial, market-leading tools. Rooted in a research project conducted for the Belgian Police, we give an overview of the main (desired) features provided or lacked by these tools, along with their measured quality in operation. Various shortcomings and suggestions for improvement will be formulated.

Assessing the state of the art of commercial tools for unstructured information exploitation

Improving the scalability of rule base verification using binary decision diagrams: An empirical study. Ki 2004 : advances in artificial intelligence. Proceedings

Jan Vanthienen

Papers

Verification and validation of knowledge-based systems with an example from site selection

Enhanced Decision Modelling through a Decision Table Engineering Workbench

Decision tables: an overview of the existing literature

Assessing the state of the art of commercial tools for unstructured information exploitation

Improving the scalability of rule base verification using binary decision diagrams: An empirical study. Ki 2004 : advances in artificial intelligence. Proceedings