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.

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

/pdf/climate-change-2007-impacts-adaptation-and-vulnerability-31xc1cf4d3.pdf

Climate Change 2007: Impacts, Adaptation and Vulnerability.

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster

Interactive email filtering learning from misclassified examples

Multi-objective optimal day-ahead scheduling of desalination-hydrogen system powered by hybrid renewable energy sources

With the increasing integration of renewable energy sources, maintaining secure and reliable power system operation becomes more challenging due to the escalated stochasticity and variations in the system. Emergency load shedding (ELS) serves as a fast and effective stability control scheme for power system after a risky disturbance occurs, which can suppress grid oscillation, recover system stability, and prevent cascading failure. Recently, data-driven techniques provide a new way to realize real-time ELS owing to their fast decision-making capability. This paper presents a risk-averse graph learning method for real-time ELS, where a graphSAGE model is proposed to fully capture the topology of power network and efficiently embed it into deep learning, and a risk-averse learning algorithm is used to avoid control failures induced by load under-cutting. The proposed method has been tested on New England 39-bus system and Nordic power system. The test results demonstrate the proposed ELS method can effectively reduce the overall control costs as compared to existing methods.

Risk-Averse Graph Learning for Real-time Power System Emergency Load Shedding

North America grid regional flow analysis

With a large number of inverter-interfaced renewable power generation, fault-induced delayed voltage recovery (FIDVR) events have become a serious threat to power system stability assessment. This study proposes a novel data-driven method based on probabilistic prediction, ensemble learning, and multi-objective optimisation programming (MOP) to rapidly predict the FIDVR severity index for real-time FIDVR assessment. Distinguished from the existing single machine learning (ML) algorithm data-driven method, the proposed method combines different randomised learning algorithms to acquire a more diversified ML outcome. The probabilistic prediction models the uncertainties existing in the prediction process, which quantifies the prediction confidence over a progressive observation window. Besides, the FIDVR can be evaluated through the time-adaptive framework to achieve the best FIDVR speed and accuracy with the MOP framework. The simulation results on the New England 10-machine 39-bus system display its preponderance over the single ML, and also demonstrate its better speed and accuracy performance in FIDVR assessment.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-gtd.2020.0402

Zhao Yang Dong

Papers

Interactive email filtering learning from misclassified examples

Multi-objective optimal day-ahead scheduling of desalination-hydrogen system powered by hybrid renewable energy sources

Risk-Averse Graph Learning for Real-time Power System Emergency Load Shedding

North America grid regional flow analysis

Hybrid randomised learning-based probabilistic data-driven method for fault-induced delayed voltage recovery assessment of power systems