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Machine Learning

Machine Learning
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.

https://www.coursera.org/course/ml

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Euclidean space This article is about Euclidean spaces of all dimensions. For 3-dimensional Euclidean space, see 3-dimensional space. A sphere, the most perfect spatial shape according to Pythagoreans, also is an important concept in modern understanding of Euclidean spaces Robot Navigation Edited by Alejandra Barrera, ISBN 978-953-307-346-0, 250 pages, Publisher: InTech, Chapters published July 05, 2011 under CC BY-NC-SA 3.0 licenseDOI: 10.5772/705 Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments.

400 Free Online Courses from Top Universities Get 1200 free online courses from the world's leading universities -- Stanford, Yale, MIT, Harvard, Berkeley, Oxford and more. You can download these audio & video courses (often from iTunes, YouTube, or university web sites) straight to your computer or mp3 player. Over 30,000 hours of free audio & video lectures, await you now. School of Engineering - Stanford Engineering Everywhere This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Wordlevel, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.

Hilbert space The state of a vibrating string can be modeled as a point in a Hilbert space. The decomposition of a vibrating string into its vibrations in distinct overtones is given by the projection of the point onto the coordinate axes in the space. Hilbert spaces arise naturally and frequently in mathematics and physics, typically as infinite-dimensional function spaces.

Introduction to Robotics - Fall 2011 This class will teach the basics of how robots can move (locomotion and kinematics), how they can sense (perception), and how they can reason about their environment (planning). Lecture materials are supported by computer exercises using the simulation software “Webots” (right). Exercises will cover programming of basic sensors, actuators and perception algorithms and are geared to prepare the students to participate in the online competition “RatsLife” ( within the framework of the class.

Automata theory An example of an automaton. The study of the mathematical properties of such automata is automata theory. Automata theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science, under Discrete mathematics (a section of Mathematics and also of Computer Science). Automata comes from the Greek word αὐτόματα meaning "self-acting".

Octave Executable versions of GNU Octave for GNU/Linux systems are provided by the individual distributions. Distributions known to package Octave include Debian, Ubuntu, Fedora, Gentoo, and openSUSE. These packages are created by volunteers. The delay between an Octave source release and the availability of a package for a particular GNU/Linux distribution varies. Advanced Robotics Spring 2013 This class is the follow-up class to CSCI3302 “Introduction to Robotics”. Robots perceive their environment with signal processing and computer vision techniques, reason about them using machine learning, artificial intelligence and discrete algorithms, and execute their actions based on constraints imposed by sensor uncertainty, their mechanism, and their dynamics. “Advanced Robotics” will teach the key concepts used by manipulating robots and provide hands-on experience with state-of-the-art software and systems. Lecture materials are supported by exercises around the “Robot Operating System” ROS and will lead to the completion of a group project. After “The Distributed Robotic Garden” at MIT, and “Robots building Robots” from 2010 to 2012, the 2013 grand challenge is to develop an autonomouse greenhouse.

Abstract machine An abstract machine, also called an abstract computer, is a theoretical model of a computer hardware or software system used in automata theory. Abstraction of computing processes is used in both the computer science and computer engineering disciplines and usually assumes discrete time paradigm. Information[edit] In the theory of computation, abstract machines are often used in thought experiments regarding computability or to analyze the complexity of algorithms (see computational complexity theory). A typical abstract machine consists of a definition in terms of input, output, and the set of allowable operations used to turn the former into the latter.

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