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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

Data Mining - Concepts and Techniques.

How to Do Things With Words

Statistics for Spatial Data.

In this paper we discuss the construction of an automated information system for a collection of visual reproductions of art objects. Special attention is payed to the economical aspects of such a system, which appears to be mainly a problem of data entry. An approach is discussed to make this feasible, which also strongly provokes consistency between descriptions. Another main target of such a system is the capability for effective disclosure. This requires a disclosure mechanism on descriptions which is easy to handle by non technical users. We show the usefulness of query by navigation for this purpose. It allows the searcher to stepwise build a query in terms of (semi-)natural language. At each step, the searcher is presented with context sensitive information.

The resulting system is described and we discuss an experiment of its use.

Documentation, Cataloging and Query by Navigation: A Practical and Sound Approach

Following an early claim by Nelson & McEvoy \cite{Nelson:McEvoy:2007} suggesting that word associations can display `spooky action at a distance behaviour', a serious investigation of the potentially quantum nature of such associations is currently underway. This paper presents a simple quantum model of a word association system. It is shown that a quantum model of word entanglement can recover aspects of both the Spreading Activation equation and the Spooky-activation-at-a-distance equation, both of which are used to model the activation level of words in human memory.

Extracting Spooky-activation-at-a-distance from Considerations of Entanglement

We utilise the quantum decision models, now well-developed in the QI community, to create a higher order social decision making model. A simple Agent Based Model (ABM) of a society of agents with changing attitudes towards a social issue is presented, where the private attitudes of individuals in the system are represented using a geometric structure inspired by quantum theory. We track the changing attitudes of the members of that society, and their resulting propensities to act, or not, in a given social context. A number of new issues surrounding this "scaling up" of quantum decision theories are discussed, as well as new directions and opportunities.

/pdf/the-quantum-inspired-modelling-of-changing-attitudes-and-b1ggt3ohsy.pdf

The quantum inspired modelling of changing attitudes and self-organising societies

This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.

Peter Bruza

Papers

Documentation, Cataloging and Query by Navigation: A Practical and Sound Approach

Extracting Spooky-activation-at-a-distance from Considerations of Entanglement

The quantum inspired modelling of changing attitudes and self-organising societies

The connected learning analytics toolkit

Quantum Models of Cognition and Decision: What is the quantum advantage? Applications to decision making