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Introduction to Theory of Computation (Anil Maheshwari, Michiel Smid)

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Individual Authors
Author: 
Anil Maheshwari, Michiel Smid
Year: 
2014
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Attribution-NonCommercial-ShareAlike 3.0 Unported
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Description: 
This is a free textbook for an undergraduate course on the Theory of Computation, which we have been teaching at Carleton University since 2002. It tries to answer the following questions: What are the mathematical properties of computer hardware and software? What is a computation and what is an algorithm? Can we give rigorous mathematical definitions of these notions? What are the limitations of computers? Can “everything” be computed?

Advanced Programming for Scientists (Dave Mason)

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Individual Authors
Author: 
Dave Mason
Year: 
2014
Conditions of Use: 
Attribution-ShareAlike 3.0 Unported
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Description: 
In this course we will learn about the Object-Oriented paradigm with particular emphasis on Java and graphical user interfaces.

(Johan Montagnat)

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Individual Authors
Author: 
Johan Montagnat
Year: 
2014
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 2.0 Generic
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Large scale distributed infrastructures leverage the high performance networks to federate computing, data and scientific resources from multiple institutions interconnected through the Internet. Distributed computing technologies have undergone a very fast evolution these last years and the infrastructure deployed have become a critical tool in many scientific disciplines. This lecture describes the foundation of distributed computing infrastructures. It introduces the main computing models exploited in Grids and Clouds to evolve from cluster computing towards more virtualized resources and across-institutional user communities. The main problems encountered when deploying such very large scale infrastructures are discussed: users identification and authorization, security of data and computations, heterogeneity of resources, redundancy and fault tolerance, deployment, management, and computation flow control… The most wide spread technologies and their associated middlewares are reviewed.

Pattern Recognition

Collection: 
OpenCourseWare de la Universidad Politécnica de Madrid
Author: 
Paco Gomez.
Year: 
2014
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 3.0 Unported
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Description: 
This is an introductory course that covers some of the most fundamental topics of exact string pattern recognition. There will be general descriptions of those topics, but there will not be an in-depth discussion of each. Instead, the course is intended to give the student an overview of the field.

Machine Learning and Computational Statistics (David Sontag )

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Individual Authors
Author: 
David Sontag
Year: 
2014
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 2.0 Generic
Level: 
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Machine learning is an exciting and fast-moving field at the intersection of computer science, statistics, and optimization with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone's Siri, digital camera face detection, Netflix recommendations, Google news). Machine learning and computational statistics also play a central role in data science. In this graduate-level class, students will learn about the theoretical foundations of machine learning and computational statistics and how to apply these to solve new problems. This is a required course for the MS in Data Science and should be taken in the first year of study; it is also suitable for MS and Ph.D. students in Computer Science and related fields.

Introduction To Machine Learning (David Sontag)

Collection: 
Individual Authors
Author: 
David Sontag
Year: 
2013
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 2.0 Generic
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Material Type: 
Description: 
Machine learning is an exciting and fast-moving field of computer science with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine (e.g., predicting protein-protein interactions, species modeling, detecting tumors, personalized medicine). In this undergraduate-level class, students will learn about the theoretical foundations of machine learning and how to apply machine learning to solve new problems.

Web Science and Web Technology (Markus Strohmaier)

Collection: 
Individual Authors
Author: 
Markus Strohmaier
Year: 
2010
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Attribution - Non Commercial - Share Alike
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Description: 
This course aims to provide students with a basic knowledge and understanding about the structure and analysis of selected web phenomena and technologies. Topics include the small world problem, network theory, social network analysis, graph search and technologies/standards/architectures such as JSON, RDF, REST and others.

An Introduction to Data Science (Jeffrey Stanton)

Collection: 
Individual Authors
Author: 
Jeffrey Stanton
Year: 
2014
Conditions of Use: 
Creative Commons Attribution-NonCommercial-ShareAlike 3.0
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Description: 
This book provides non-technical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language for statistical computing and graphics.

XML with Java, Java Servlet, and JSP

Collection: 
Harvard Open Learning Initiative
Author: 
David Malan
Year: 
2007
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 3.0 Unported
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Description: 
This course introduces XML as a key enabling technology in Java-based applications. Students learn the fundamentals of XML and its derivatives, including DTD, SVG, XML Schema, XPath, XQuery, XSL-FO, and XSLT. Students also gain experience with programmatic interfaces to XML like SAX and DOM, standard APIs like JAXP and TrAX, and industry-standard software like Ant, Tomcat, Xerces, and Xalan. The course acquaints students with J2EE, including JavaServer Pages (JSP) and Java Servlet, and also explores HTTP, SOAP, web services, and WSDL. The course's projects focus on the implementation and deployment of these technologies.
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Advanced Robotics (Pieter Abbeel)

Collection: 
Individual Authors
Author: 
Pieter Abbeel
Year: 
2011
Conditions of Use: 
Attribution 3.0 Unported
Level: 
Media Format: 
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Description: 
This is a graduate course on robotics for computers scientists and those with an engineering or physics background. Course topics include: Estimation: Bayes filters, KF, EKF, UKF, particle filter, occupancy grid mapping, EKF slam, GraphSLAM, SEIF, FastSLAM; Optimal Control: Globally Optimal Control through Discretization, Locally Optimal Control through Sequential Quadratic Programming, MPC, LQR, LQG, iterative versions;Motion Planning: RRT , A* RGB-D and Point Clouds: features, Ransac, Hough, instance retrieval/detection; Manipulation and Grasping: grasp quality metrics, grasp strategies, caging; Reinforcement Learning: policy gradient

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