There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Determining the most influential human factors in maritime accidents: A data-driven approach

We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of the sensitive attribute. We consider the case that the sensitive attribute is available for prediction. We establish a connection between fair regression and optimal transport theory, based on which we derive a close form expression for the optimal fair predictor. Specifically, we show that the distribution of this optimum is the Wasserstein barycenter of the distributions induced by the standard regression function on the sensitive groups. This result offers an intuitive interpretation of the optimal fair prediction and suggests a simple post-processing algorithm to achieve fairness. We establish risk and distribution-free fairness guarantees for this procedure. Numerical experiments indicate that our method is very effective in learning fair models, with a relative increase in error rate that is inferior to the relative gain in fairness.

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Fair regression with Wasserstein barycenters

The growing interest in recent years towards Learning An- alytics (LA) and Educational Data Mining (EDM) has enabled novel ap- proaches and advancements in educational settings. The wide variety of research and practice in this context has enforced important possibilities and applications from adaptation and personalization of Technology En- hanced Learning (TEL) systems to improvement of instructional design and pedagogy choices based on students needs. LA and EDM play an im- portant role in enhancing learning processes by oering innovative methods of development and integration of more personalized, adaptive, and inter- active educational environments. This has motivated the organization of the ESANN 2015 Special Session in Advances in Learning Analytics and Educational Data Mining. Here, a review of research and practice in LA and EDM is presented accompanied by the most central methods, bene- ts, and challenges of the eld. Additionally, this paper covers a review of novel contributions into the Special Session.

Advances in learning analytics and educational data mining

In-sample model selection for Support Vector Machines is a promising approach that allows using the training set both for learning the classifier and tuning its hyperparameters. This is a welcome improvement respect to out-of-sample methods, like cross-validation, which require to remove some samples from the training set and use them only for model selection purposes. Unfortunately, in-sample methods require a precise control of the classifier function space, which can be achieved only through an unconventional SVM formulation, based on Ivanov regularization. We prove in this work that, even in this case, it is possible to exploit well-known Quadratic Programming solvers like, for example, Sequential Minimal Optimization, so improving the applicability of the in-sample approach.

Luca Oneto

Papers

Determining the most influential human factors in maritime accidents: A data-driven approach

Fair regression with Wasserstein barycenters

Advances in learning analytics and educational data mining

In-sample model selection for Support Vector Machines

Local Rademacher Complexity