Several new content pieces invite you to do hands-on work with web GIS technology:
 10 Things you can do with ArcGIS Online in education. These include: (1) Use web mapping applications. (2) Make your own map. (3) Get a school, club, or university organizational account in ArcGIS Online. (4) Use and modify existing curricular resources. (5) Explore the Living Atlas of the World. (6) Modify and ask questions of maps. (7) Conduct spatial analysis on mapped data. (8) Add multimedia to maps. (9) Explore your world in 3D, and (10) Map and analyze field-collected data.
 Introduction and Advanced Work with Story Maps: Slides and hands-on exercises. These include how to build a story map from a web map, and how to build map tours, map journals, swipe, series, and other types of story maps.
 Teaching with Web Apps. Set of resources and activities. These include examining Pacific typhoons in 3D, demographics of Zip Codes, creating viewsheds and buffers, and much more.
 Spatial Analysis in Human Geography. These include the 1854 cholera epidemic in London (activity), a Boulder County hazards analysis (map), and an examination of the Human Development Index around the world (map).
I created this content for the Esri mapping lab for the 2017 National Conference on Geography Education, but it can also be used to support your own professional development or for your own instruction.
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Several new content pieces invite you to do hands-on work with web GIS technology:
A quickstart tutorial to the IAR Workbench IDE. Learn how to create a project, edit files, build solutions, and use the debugger.
This module includes the basics and theories of ICT, including types of computer, networks, how, why and who people access information using ICT. This module is the first under the ECDL (AKA ICDL) qualification, written for Windows XP and Office 2003
This collection provides an overview of the 2008-'09 Open Education Cup competition. Contest rules, author resources, and example content are provided. This competition is intended to encourage development of original educational content in the field of parallel computing, with cash prizes awarded to contest winners. Selected modules will be included as part of a new collection available through Connexions.
Advances in artificial intelligence are triggering discussions about our future and potential consequences, both good and bad. World leaders are engaging in the conversation as well. Wired Editor-in-Chief, Scott Dadich, and MIT Media Lab Director, Joi Ito, conducted an interview with President Obama on artificial intelligence and society.
In this case study, you will explore an article and video from Wired's conversation with President Obama; then you will be asked to reflect on the core ideas and details of the conversation. As engaged and informed citizens, we must begin to consider the relationship between artificial intelligence and society.
Introductory lesson for AP Computer Science A course into concepts of if-statements and String manipulation in conjunction with the AP Magpie Chatbot Lab. This lab is used early within the AP Computer Science A course, though prerequisite knowledge is listed within the lesson. This lesson utilizes outside coding resources which are publicly available through the AP labs.
This subset of the Black Box Software Testing collection includes resources to teach quality cost analysis, combination testing, regression testing, GUI regression automation, high volume test automation, requirements analysis, test documentation, test scripts, and scripted testing and inattentional blindness. Resources include lecture videos, slides, activities, and suggested readings.
The TI TMS320C54x microprocessor provides a number of ways to specify the location of data to be used in calculations. Immediate addressing, direct addressing, and indirect addressing are the three main types. Knowing the basic addressing modes of a mic
" This is a graduate course on the design and analysis of algorithms, covering several advanced topics not studied in typical introductory courses on algorithms. It is especially designed for doctoral students interested in theoretical computer science."
This course will present advanced topics in Artificial Intelligence (AI), including inquiries into logic, artificial neural network and machine learning, and the Turing machine. Upon successful completion of this course, students will be able to: define the term 'intelligent agent,' list major problems in AI, and identify the major approaches to AI; translate problems into graphs and encode the procedures that search the solutions with the graph data structures; explain the differences between various types of logic and basic statistical tools used in AI; list the different types of learning algorithms and explain why they are different; list the most common methods of statistical learning and classification and explain the basic differences between them; describe the components of Turing machine; name the most important propositions in the philosophy of AI; list the major issues pertaining to the creation of machine consciousness; design a reasonable software agent with java code. (Computer Science 408)
This is the final installment of my three part tutorial on the CNXML language. It is currently valid for the most recent release of the 0.3 language. The keywords contain a list of the tags described in this tutorial. Along with the example code in this module there is also an example module that has been growing throughout the tutorial.
Following a brief classroom discussion of relevant principles, each student completes the paper design of several advanced circuits such as multiplexers, sample-and-holds, gain-controlled amplifiers, analog multipliers, digital-to-analog or analog-to-digital converters, and power amplifiers. One of each student's designs is presented to the class, and one may be built and evaluated. Associated laboratory emphasizing the use of modern analog building blocks. Alternate years.
This course will expand upon SQL as well as other advanced topics, including query optimization, concurrency, data warehouses, object-oriented extensions, and XML. Additional topics covered in this course will help you become more proficient in writing queries and will expand your knowledge base so that you have a better understanding of the field. Upon successful completion of this course, the student will be able to: write complex queries, including full outer joins, self-joins, sub queries, and set theoretic queries; write stored procedures and triggers; apply the principles of query optimization to a database schema; explain the various types of locking mechanisms utilized within database management systems; explain the different types of database failures as well as the methods used to recover from these failures; design queries against a distributed database management system; perform queries against database designed with object-relational extensions; develop and query XML files. (Computer Science 410)
Materials covered include: special relativity, electrodynamics of moving media, waves in dispersive media, microstrip integrated circuits, quantum optics, remote sensing, radiative transfer theory, scattering by rough surfaces, effective permittivities, and random media.
This course is a graduate introduction to natural language processing - the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. The subject qualifies as an Artificial Intelligence and Applications concentration subject.
Recent results in cryptography and interactive proofs. Lectures by instructor, invited speakers, and students. Alternate years. The topics covered in this course include interactive proofs, zero-knowledge proofs, zero-knowledge proofs of knowledge, non-interactive zero-knowledge proofs, secure protocols, two-party secure computation, multiparty secure computation, and chosen-ciphertext security.
" This course covers concepts and techniques for the design and implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications. Topics include combinators, generic operations, pattern matching, pattern-directed invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement. Substantial weekly programming Assignments and Labs are an integral part of the subject. There will be extensive programming Assignments and Labs, using MIT/GNU Scheme. Students should have significant programming experience in Scheme, Common Lisp, Haskell, CAML or some other "functional" language."
Building on Complex Adaptive Systems theory and basic Agent Based Modeling knowledge presented in SPM4530, the Advanced course will focus on the model development process. The students are expected to conceptualize, develop and verify a model during the course, individually or in a group. The modeling tasks will be, as much as possible, based on real life research problems, formulated by various research groups from within and outside the faculty.
Study Goals The main goal of the course is to learn how to form a modeling question, perform a system decomposition, conceptualize and formalize the system elements, implement and verify the simulation and validate an Agent Based Model of a socio-technical system.
AlgoViz.org is a gathering place for users and developers of algorithm visualizations and animations (AVs). It is a gateway to AV-related services, collections, and resources. AVs are grouped by topic and subjective evaluation data on the visualizations from a pedagogic perspective is often included. The site provides links to AV-related research literature. There are forums to discuss using, developing and teaching with AVs.
This course focuses on the fundamentals of computer algorithms, emphasizing methods useful in practice. Upon successful completion of this course, the student will be able to: explain and identify the importance of algorithms in modern computing systems and their place as a technology in the computing industry; indentify algorithms as a pseudo-code to solve some common problems; describe asymptotic notations for bounding algorithm running times from above and below; explain methods for solving recurrences useful in describing running times of recursive algorithms; explain the use of Master Theorem in describing running times of recursive algorithms; describe the divide-and-conquer recursive technique for solving a class of problems; describe sorting algorithms and their runtime complexity analysis; describe the dynamic programming technique for solving a class of problems; describe greedy algorithms and their applications; describe concepts in graph theory, graph-based algorithms, and their analysis; describe tree-based algorithms and their analysis; explain the classification of difficult computer science problems as belonging to P, NP, and NP-hard classes. (Computer Science 303)
This is a textbook for first year Computer Science. Algorithms and Data Structures With Applications to Graphics and Geometry.
This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.
In-depth study of an active research topic in computer graphics. Topics change each term. Readings from the literature, student presentations, short assignments, and a programming project. Animation is a compelling and effective form of expression; it engages viewers and makes difficult concepts easier to grasp. Today's animation industry creates films, special effects, and games with stunning visual detail and quality. This graduate class will investigate the algorithms that make these animations possible: keyframing, inverse kinematics, physical simulation, optimization, optimal control, motion capture, and data-driven methods. Our study will also reveal the shortcomings of these sophisticated tools. The students will propose improvements and explore new methods for computer animation in semester-long research projects. The course should appeal to both students with general interest in computer graphics and students interested in new applications of machine learning, robotics, biomechanics, physics, applied mathematics and scientific computing.
This course will provide an overview of a new vision for Human-Computer Interaction (HCI) in which people are surrounded by intelligent and intuitive interfaces embedded in the everyday objects around them. It will focus on understanding enabling technologies and studying applications and experiments, and, to a lesser extent, it will address the socio-cultural impact. Students will read and discuss the most relevant articles in related areas: smart environments, smart networked objects, augmented and mixed realities, ubiquitous computing, pervasive computing, tangible computing, intelligent interfaces and wearable computing. Finally, they will be asked to come up with new ideas and start innovative projects in this area.
Device and circuit level optimization of digital building blocks. MOS and bipolar device models and second order effects. Circuit design styles and arithmetic structures. Estimation and minimization of energy consumption. Interconnect models and parasitics; driver design; timing issues (clock skew, self-timed circuits, etc.). Memory architectures, circuits (sense amplifiers) and devices. Testing of integrated circuits. Extensive use of circuit layout and SPICE in design projects and software labs.
In the first of two sequential lessons, students create mobile apps that collect data from an Android device's accelerometer and then store that data to a database. This lesson provides practice with MIT's App Inventor software and culminates with students writing their own apps for measuring acceleration. In the second lesson, students are given an app for an Android device, which measures acceleration. They investigate acceleration by collecting acceleration vs. time data using the accelerometer of a sliding Android device. Then they use the data to create velocity vs. time graphs and approximate the maximum velocity of the device.
Trabajo Final para la cátedra Diseño de Sistemas de Tiempo Real. En este trabajo se muestra desde cómo armar el robot con las piezas compradas hasta cómo modificar y adaptar RTuinOS (un SO de Tiempo Real) para que funcione en nuestro Arduino.
The items in this collection were developed by the author for the support of open courseware. Some modules are particularly useful for students taking courses that use computers and others could be used for all courses.
Phenomenological approach to superconductivity, with emphasis on superconducting electronics. Electrodynamics of superconductors, London's model, and flux quantization. Josephson Junctions and superconducting quantum devices, equivalent circuits, and high-speed superconducting electronics. Quantized circuits for quantum computing. Overview of type II superconductors, critical magnetic fields, pinning, the critical state model, superconducting materials, and microscopic theory of superconductivity. Alternate years.
Este libro está dirigido, principalmente, a Estudiantes y Docentes que quieren aprender a programar como forma de fortalecer sus capacidades cognoscitivas y así obtener un beneficio adicional de su computador para lograr un mejor provecho de sus estudios. Dada la orientación del libro respecto a programar para resolver problemas asociados a las Ciencias e Ingenierías, el requisito mínimo de matemáticas que hemos elegido para presentar el contenido del mismo se cubre, normalmente, en el tercer año del bachillerato. No obstante, el requisito no es obligatorio para leer el libro en su totalidad y adquirir los conocimientos de programación obviando el contenido matemático.
- Computer Science
- Material Type:
- Project LATIn: The Latin American Open Textbook Initiative
- Héctor Fernández
- Juan Carlos Ruiz
- Sergio Rojas
An quick overview of AI from both the technical and the philosophical points of view. Topics discussed include search, A*, Knowledge Representation, Neural Nets. Video of each class is available, as are problem sets.
This course includes materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics, which will introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence. The material is introductory; the readings cite many resources outside those assigned in this course, and students are encouraged to explore these resources to pursue topics of interest. Upon successful completion of this course, the student will be able to: Describe the major applications, topics, and research areas of artificial intelligence (AI), including search, machine learning, knowledge representation and inference, natural language processing, vision, and robotics; Apply basic techniques of AI in computational solutions to problems; Discuss the role of AI research areas in growing the understanding of human intelligence; Identify the boundaries of the capabilities of current AI systems. (Computer Science 405)
An introduction to the main techniques of Artifical Intelligence: state-space search methods, semantic networks, theorem-proving and production rule systems. Important applications of these techniques are presented. Students are expected to write programs exemplifying some of techniques taught, using the LISP lanuage.
This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.