Introduction to Machine Learning

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The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation.


Deep Learning: Definition, Resources, Comparison with Machine Learning

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Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car. Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) -- even though they appear in new contexts such as IoT or machine to machine communication -- still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others. Some are new, such as indexation algorithms to automate digital publishing, improve search engines, or create and manage large catalogs such as Amazon's product listing. As a result, many deep learning practitioners call themselves data scientist, computer scientist, statistician, or sometimes engineer.


Deep Learning: Definition, Resources, Comparison with Machine Learning

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Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car. Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) -- even though they appear in new contexts such as IoT or machine to machine communication -- still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others. Some are new, such as indexation algorithms to automate digital publishing, improve search engines, or create and manage large catalogs such as Amazon's product listing. As a result, many deep learning practitioners call themselves data scientist, computer scientist, statistician, or sometimes engineer.


Big Data Analytics with SAS

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The Fourth Industrial Revolution is upon us, even with the Third is still in progress. Big Data, Machine Learning and Artificial Intelligence are three of the driving forces behind it. While the term'Industrial Revolution' has always applied mainly to manufacturing, it now also involves service industries such as banking and insurance, who are investing heavily in Big Data to help them model credit risk, fraud, marketing success and other key data. Meanwhile manufacturing, retail, telco, pharma and many other sectors constantly need people skilled in building, analysing, monitoring and maintaining data models to gain strategic intelligence that helps them inform and adapt their key business processes. A leader in the world of Data Analytics is the SAS Institute, whose flagship product is SAS (Statistical Analysis System).


Superintelligence: Paths, Dangers, Strategies eBook: Nick Bostrom: Amazon.it: Kindle Store

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Prof. Bostrom has written a book that I believe will become a classic within that subarea of Artificial Intelligence (AI) concerned with the existential dangers that could threaten humanity as the result of the development of artificial forms of intelligence. What fascinated me is that Bostrom has approached the existential danger of AI from a perspective that, although I am an AI professor, I had never really examined in any detail. When I was a graduate student in the early 80s, studying for my PhD in AI, I came upon comments made in the 1960s (by AI leaders such as Marvin Minsky and John McCarthy) in which they mused that, if an artificially intelligent entity could improve its own design, then that improved version could generate an even better design, and so on, resulting in a kind of "chain-reaction explosion" of ever-increasing intelligence, until this entity would have achieved "superintelligence". This chain-reaction problem is the one that Bostrom focusses on. He sees three main paths to superintelligence: 1. The AI path -- In this path, all current (and future) AI technologies, such as machine learning, Bayesian networks, artificial neural networks, evolutionary programming, etc. are applied to bring about a superintelligence.