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. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. lesen.to Hier klicken für den garantierten Download von "Yára Detert – Mathematik für Ahnungslose" (kostenlose Anmeldung erforderlich ->hier<-) Lösen von Gleichungen höherer Ordnung – Eulersche Formel – Multiplikation von Matrizen. Wie war denn das noch? Format: pdf | Größe: 15,3 MBFirstload.de: DownloadUploaded.to: DownloadShare-Online.biz: DownloadTurbobit.net: Download Passwort: lesen.to | Uploader: acore
BMW Forecasts Cars Will Be Highly Automated by 2020, Driverless by 2025. The latest in a slew of press from major automakers, BMW and automotive supplier Continental recently announced a partnership to develop new technology for self-driving cars. The collaboration aims to develop an “electronic co-pilot” system for highway grade driverless cars over the next year. And the announcement came with a bold forecast: partially automated cars by 2016, highly automated cars by 2020, and fully automated cars by 2025. During their year-long partnership, BMW and Continental will build and test several prototypes using near-production technology—stereo cameras, radar, and laser sensors. This isn’t BMW’s first foray into automation by any means. Beyond BMW, other commercial automakers gunning for full automation include Audi and Mercedes. Given the competition—that 2025 forecast may prove too conservative. Perennially unimpressed Peter Thiel famously complains, “We wanted flying cars, instead we got 140 characters.” And maybe that’s as it should be.
Mendel HMM Toolbox for Matlab Written by Steinar Thorvaldsen, 2004. Last updated: Jan. 2006. Dept. of Mathematics and Statistics University of Tromsø - Norway. steinart@math.uit.no MendelHMM is a Hidden Markov Model (HMM) tutorial toolbox for Matlab. To run the program you should make the following steps: 1. When you type "mendelHMM" in Matlab command window the main window of GUI will appear. Main window of the program. In his historic experiment, Gregor Mendel (1822-1884) examined 7 simple traits in the common garden pea (Pisum). Today we know that the recessive expressions most often are mutations in the DNA molecule of the gene, as it is well known for Mendel’s growth gene (trait 7) where a single nucleotide G is substituted with an A. In his experiment Mendel also studied in more detail the plant seeds with two and three heredity factors simultaneously. The estimate of a statistical model according to a training set There are two main types of learning. The sampling of new training data y = (A, A, a, a, a) 1. 2. 3.
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.
Type Fu - touch typing trainer, tutor and test Technology - Robot racing car aims for pole position Meet the 120mph sports car that thunders around tracks without any help from a driver. Self-driving cars have the headlines recently for everything from navigating busy streets to their ability to park themselves. But researchers at Stanford University in California are moving faster, with a self-driving racing car, capable of speeds up to 120mph (190km/h). Engineers at the university have have developed an autonomous Audi TT-S, named Shelly, which can drive at the limits of vehicle performance. The car recently powered around the Thunderhill Raceway in California, clocking a time only a few seconds slower than a human. Professor Chris Gerdes, the main researcher behind the project, recently joined me on stage at the Atlantic Big Science Summit in Silicon Valley. Jon Stewart: Is this car completely autonomous - there’s no remote control or human interference? Chris Gerdes: That’s absolutely correct. JS: Tell us about some of the technology it uses to do that.
General Hidden Markov Model Library | Free Science & Engineering software downloads Introduction to Robotics - Fall 2011 | Correll Lab 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. In RatsLife, two miniature robots “E-Puck” are competing against each other in a virtual maze for available chargers. The students will work in teams of 2 to 3 and develop controllers for the robots putting concepts taught in class into practice. Students will also have the ability to launch their controllers on a set of real e-Puck robots. Prerequisites: programming experience in C/C++ and/or Java. The “Introduction class” is offered as CSCI 3302 and ECEE 3303 in Fall 2011.
Autonomous Plane Lands on Aircraft Carrier Aircraft that fly autonomously, without a pilot on board or even on the ground are taking to the air. Now the U.S. Navy is pushing a robotic airplane to do something many human pilots never master: land on an aircraft carrier. The plane is the X-47B, and it's the first aircraft that flies completely on its own. The flight took place at the Naval Air Station at Patuxent River, Md., (otherwise known as Pax River), with the plane taking a quick jaunt around Chesapeake Bay. PHOTOS: Five Scariest Bioweapons Unlike the more familiar Predator drones, the X-47B doesn't need a pilot in a control room. The plane didn't go as fast as many unmanned aircraft currently in use –- it hit a speed of 180 knots, or about 200 miles an hour, whereas some drones hit speeds twice that. It's the first flight by the U.S. BLOG: Pentagon Tests Hypersonic Flying Bomb Sometime next year the Navy hopes to test it aboard a real carrier, though it will have to prove itself at Pax River first. via: United States Navy
Online Code Repository The goal is to have working code for all the algorithms in the book in a variety of languages. So far, we have Java, Lisp and Python versions of most of the algorithms. There is also some old code in C++, C# and Prolog, but these are not being maintained. We also have a directory full of data files. Let peter@norvig.com know what languages you'd like to see, and if you're willing to help. Supported Implementations We offer the following three language choices, plus a selection of data that works with all the implementations: Java: aima-java project, by Ravi Mohan. Unsupported Implementations Implementation Choices What languages are instructors recommending? Of course, neither recall nor precision is perfect for these queries, nor is the estimated number of results guaranteed to be accurate, but they offer a rough estimate of popularity.
Advanced Robotics Spring 2013 | Correll Lab 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. Meetings: The class will meet MWF from 1-1.50pm in ECR 108 and in ECCS 1B21 for exercises. Due dates: This class has three two week homework assignments. This is a 4830/7000 “Special Topics” class. Prerequisites: CSCI3302 (only for CS4830 students) The overarching goal of this class is to implement an autonomous greenhouse. Final Projects 1.