Cognitive science arose in the 1950s when it became apparent that a number of disciplines, including psychology, computer science, linguistics, and philosophy, were fragmenting. Perhaps owing to the field’s immediate origins in cybernetics, as well as to the foundational assumption that cognition is information processing, cognitive science initially seemed more unified than psychology. However, as a result of differing interpretations of the foundational assumption and dramatically divergent views of the meaning of the term information processing, three separate schools emerged: classical cognitive science, connectionist cognitive science, and embodied cognitive science.

Increasingly, we are realizing that to make computer systems more intelligent and responsive to users, we will have to make them more sensitive to context. Traditional hardware and software design overlooks context because it conceptualizes systems as input-output functions. Systems take input explicitly given to them by a human, act upon that input alone and produce explicit output. But this view is too restrictive. Smart computers, intelligent agent software, and digital devices of the future will also have to operate on data that they observe or gather for themselves. They may have to sense their environment, decide which aspects of a situation are really important, and infer the user’s intention from concrete actions. The system’s actions may be dependent on time, place, or the history of interaction, in other words, dependent upon context.
But what exactly is context? We’ll look at perspectives from machine learning, sensors and embedded devices, information visualization, philosophy and psychology. We’ll see how each treats the problem of context, and discuss the implications for design of context-sensitive hardware and software.
Course requirements will consist of critiques of class readings (about 3 papers/week), and a final project (paper or computer implementation project).

This book was written for students and instructors who want to learn how to use a computer for other than the most common uses, such as web browsing, document creation, or paying bills online. This book is for anyone who wants to perform computational tasks that they design. In other words, if you wish to learn how to program a computer, this book is for you.

Because prealgebra is a subject that practically everyone is supposed to learn in grade school, it provides a platform to introduce basic computer programming concepts. Consequently, this book should also be of interest to students in middle or high school who want to learn how to program, and who are willing to invest the time and effort in learning a programming language that they could continue using throughout their schooling and in their professional life. Similarly, this book could also be of interest to pre-service and in-service mathematics teachers wishing to have at their disposal a complementary tool to assist in fostering understanding, competency, and interest in mathematics among their students. This book can be integrated with the teachers’ curriculum as way to tackle non-traditional math problems using an inexpensive modern computer language. By the end of the book, a reader will have learned enough to be able to write a preliminary, step-by-step one variable equation solver that can be expanded in the future to use with more complex equations. In other words, by the end of the book, you will be able to write code that programs their machines to solve equations. This code is foundational and readers are ecouraged to learn on their own how to build on it to suit their mathematics learning needs.

This lesson focuses on the biggest problem faced by any young programmer - i.e. the LOGIC BUILDING required while solving a particular problem. With programming, the solution to a particular problem lies in the head, but one is unable to convert it into a computer program. This is because the thought processes of a human are much faster than the sense of observation. If this thought process could be slowed down, logic to solve a programming problem could be found very easily. This lesson focuses on converting this psychological thought process in a step-by -step logic fashion that a computer program can understand. This lesson is recorded in a kitchen where the basic programming concepts are taught by giving examples from the process of making a mango milk shake. This lesson teaches the 4 following techniques: 1) Swapping two variables by swapping a glass of milk with a glass of crushed ice; 2) Finding max from an array by finding the biggest mango; 3) Sorting an array by arranging the jars; and 4) Understanding the concept of a function, parameters and return type by comparing it with the blender/juicer. The lesson targets those students who know the syntax of programming in any language (C or GWBASIC preferred), but are unable to build the logic for a program. It can be taught in a class of 45 to 50 minutes.

This lesson is also available in Mandarin Chinese.

Students learn that ordinary citizens, including students like themselves, can make meaningful contributions to science through the concept of "citizen science." First, students learn some examples of ongoing citizen science projects that are common around the world, such as medical research, medication testing and donating idle computer time to perform scientific calculations. Then they explore Zooniverse, an interactive website that shows how research in areas from marine biology to astronomy leverage the power of the Internet to use the assistance of non-scientists to classify large amounts of data that is unclassifiable by machines for various reasons. To conclude, student groups act as engineering teams to brainstorm projects ideas for their own town that could benefit from community help, then design conceptual interactive websites that could organize and support the projects.

A google drive of resources created by Mary Newberg for CHEMI 0485 - Basic Laboratory and Computation Chemistry at College of DuPage. These labs are designed for students working under the supervision of an instructor in a college chemistry laboratory.

Lecture #7 for the course: CSCI 49378: Intro to Distributed Systems and Cloud Computing - "Cloud Systems and Infrastructures (Part One)". Delivered at Hunter College in Spring 2020 by Bonan Liu as part of the Tech-in-Residence Corps program.

This course provides an introductory survey of data science tools in healthcare. It was created by members of MIT Critical Data, a global consortium consisting of healthcare practitioners, computer scientists, and engineers from academia, industry, and government, that seeks to place data and research at the front and center of healthcare operations.
The most daunting global health issues right now are the result of interconnected crises. In this course, we highlight the importance of a multidisciplinary approach to health data science. It is intended for front-line clinicians and public health practitioners, as well as computer scientists, engineers, and social scientists, whose goal is to understand health and disease better using digital data captured in the process of care.
What you’ll learn:

Principles of data science as applied to health
Analysis of electronic health records
Artificial intelligence and machine learning in healthcare

This course is part of the Open Learning Library, which is free to use. You have the option to sign up and enroll in the course if you want to track your progress, or you can view and use all the materials without enrolling.

Think Java is a hands-on introduction to computer science and programming used by many universities and high schools around the world. Its conciseness, emphasis on vocabulary, and informal tone make it particularly appealing for readers with little or no experience. The book starts with the most basic programming concepts and gradually works its way to advanced object-oriented techniques. In this fully updated and expanded edition, authors Allen Downey and Chris Mayfield introduce programming as a means for solving interesting problems. Each chapter presents material for one week of a college course and includes exercises to help you practice what you’ve learned. Along the way, you’ll see nearly every topic required for the AP Computer Science A exam and Java SE Programmer I certification.

Short Description:
This course will introduce the student to Microsoft windows, Word, Excel, Access and PowerPoint.

Long Description:
This textbook will introduce the student to Microsoft windows, Word, Excel, Access and PowerPoint. The text covers basic concepts of creating word processing, spreadsheets, databases and presentation materials for the workplace.

Word Count: 26424

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

This course covers abstractions and implementation techniques for the design of distributed systems. Topics include: server design, network programming, naming, storage systems, security, and fault tolerance. The assigned readings for the course are from current literature. This course is worth 6 Engineering Design Points.

Dieter Hartmann, a high-energy physicist, presents a story-based lesson on the science of Gamma-Ray astronomy. The lesson focuses on gamma-ray bursts; examining their sources, types, and links to the origin and evolution of the Universe. The story-based format of the lesson also provides insights into the nature of science. Students answer questions based on the reading guide. A list of supplemental websites is also included.

In the previous video we looked at the basics of operating systems. In this video we take a look at the current operating systems available to consumer. In addition to looking at Windows, we also cover the current Mac OS X, Linux and Mobile Operating systems.

Links from video:
http://www.ubuntu.com/
http://knopper.net/knoppix/index-en.html

This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages.

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming.

This class is an applications-oriented course covering the modeling of large-scale systems in decision-making domains and the optimization of such systems using state-of-the-art optimization tools. Application domains include: transportation and logistics planning, pattern classification and image processing, data mining, design of structures, scheduling in large systems, supply-chain management, financial engineering, and telecommunications systems planning. Modeling tools and techniques include linear, network, discrete and nonlinear optimization, heuristic methods, sensitivity and post-optimality analysis, decomposition methods for large-scale systems, and stochastic optimization.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5223 (System Optimisation: Models and Computation).

Students learn how 3D printing, also known as additive manufacturing, is revolutionizing the manufacturing process. First, students learn what considerations to make in the engineering design process to print an object with quality and to scale. Students learn the basic principles of how a computer-aided design (CAD) model is converted to a series of data points then turned into a program that operates the 3D printer. The activity takes students through a step-by-step process on how a computer can control a manufacturing process through defined data points. Within this activity, students also learn how to program using basic G-code to create a wireframe 3D shapes that can be read by a 3D printer or computer numerical control (CNC) machine.

This is the website for “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.

This course will focus on understanding aspects of modern technology displaying exponential growth curves and the impact on global quality of life through a weekly updated class project integrating knowledge and providing practical tools for political and business decision-making concerning new aspects of bioengineering, personalized medicine, genetically modified organisms, and stem cells. Interplays of economic, ethical, ecological, and biophysical modeling will be explored through multi-disciplinary teams of students, and individual brief reports.