Our learning task: Given a training set S {(x1 , y1), (x2 , y2), . . . The simplest case: y { 1, 1} called binary classification problem If y is a real number it becomes a regression problem More general case, y can be a vector and each element is drawn from a finite set.

Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed. We can make machines learn to do things! The first time I heard that, it blew my mind. That means that we can program computers to learn things by themselves! The ability of learning is one of the most important aspects of intelligence. Translating that power to machines, sounds like a huge step towards making them more intelligent. And in fact, Machine Learning is the area that is making most of the progress in Artificial Intelligence today; being a trendy topic right now and pushing the possibility to have more intelligent machines.

Causal learning is an inductive process and causal knowledge about the world is of paramount importance for intelligent systems, natural or artificial. Given an observation of events happening in the world, how does an intelligent system establish the causalities between them? The issue is further complicated by intervening noisy events. Psychologists have proposed a contingency model of causal induction but it does not incorporate computational means of addressing the issues of intervening noise to recover the causalities between events. In this paper we propose an inductive causal learning method that is able to establish causalities between events in the presence of intervening noisy events, and we apply the method to real-world data to investigate its viability. We demonstrate that the learning method works well in uncovering valid causalities, and relatively non-noisy, opportunistic situations provide the best confirmation of the causalities involved. Causal knowledge is the foundation of problem solving and the ability to learn causal knowledge enables the intelligent system to be maximally adaptive.

Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates that suspicious activity is successfully detected. Importantly, the system exhibits a low rate of false positives, and is therefore suitable for use in a live intelligence environment.

Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).[1] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[2] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions,[4]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[5] optical character recognition (OCR),[6] search engines and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[7] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[4]:vii[8]

Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not an easy task owing to their variability and ambiguity due to thousands of micro and pico-plankton species. Therefore, the aim of this paper is to build a framework for identifying phytoplankton species and to perform a comparison on different features types and classifiers. We propose a new features type extracted from raw signals of phytoplankton species. We then analyze the performance of various classifiers on the proposed features type as well as two other features types for finding the robust one. Through experiments, it is found that Random Forest using the proposed features gives the best classification results with average accuracy up to 98.24%.

The birth of artificial-intelligence research as an autonomous discipline is generally thought to have been the monthlong Dartmouth Summer Research Project on Artificial Intelligence in 1956, which convened 10 leading electrical engineers -- including MIT's Marvin Minsky and Claude Shannon -- to discuss "how to make machines use language" and "form abstractions and concepts." A decade later, impressed by rapid advances in the design of digital computers, Minsky was emboldened to declare that "within a generation ... the problem of creating'artificial intelligence' will substantially be solved." The problem, of course, turned out to be much more difficult than AI's pioneers had imagined. In recent years, by exploiting machine learning -- in which computers learn to perform tasks from sets of training examples -- artificial-intelligence researchers have built special-purpose systems that can do things like interpret spoken language or play Jeopardy with great success. But according to Tomaso Poggio, the Eugene McDermott Professor of Brain Sciences and Human Behavior at MIT, "These recent achievements have, ironically, underscored the limitations of computer science and artificial intelligence. We do not yet understand how the brain gives rise to intelligence, nor do we know how to build machines that are as broadly intelligent as we are."

The C5.0 algorithm uses the Max-Gain method of selecting the best attribute. H measures the information content or entropy in bits (i.e., number of yes/no questions that must be asked) associated with a set S of examples, which consists of the subset P of positive examples and subset N of negative examples. Note: 0 H(P,N) 1, where 0 no information, and 1 maximum information. Half the examples in S are positive and half are negative. Say all of the examples in S are positive and none are negative.

Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence).

Time series forecasting can be framed as a supervised learning problem. This re-framing of your time series data allows you access to the suite of standard linear and nonlinear machine learning algorithms on your problem. In this post, you will discover how you can re-frame your time series problem as a supervised learning problem for machine learning. Time Series Forecasting as Supervised Learning Photo by Jeroen Looyé, some rights reserved. The majority of practical machine learning uses supervised learning. Supervised learning is where you have input variables (X) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output.