This activity utilizes a Claim-Support-Question framework that allows students to draw on prior knowledge, investigation, and questioning. Designed to be placed within a Java context of enhancing object-oriented programming classes using interfaceses, students will analyze, implement, and support claims regarding three separate interfaces while moving between analysis and program implementation.

Scientists who are working to discover new medicines often use robots to prepare samples of cells, allowing them to test chemicals to identify those that might be used to treat diseases. Students will meet a scientist who works to identify new medicines. She created free software that ''looks'' at images of cells and determines which images show cells that have responded to the potential medicines. Students will learn about how this technology is currently enabling research to identify new antibiotics to treat tuberculosis. Students will complete hands-on activities that demonstrate how new medicines can be discovered using robots and computer software, starring the student as ''the computer.'' In the process, the students learn about experimental design, including positive and negative controls.

Lecture #3 for the course: CSCI 49378: Intro to Distributed Systems and Cloud Computing - "Synchronization, Consistency and Replication". Delivered at Hunter College in Spring 2020 by Bonan Liu as part of the Tech-in-Residence Corps program.

This course uses the theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Specific topics include: energy models from classical potentials to first-principles approaches; density functional theory and the total-energy pseudopotential method; errors and accuracy of quantitative predictions: thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations; free energy and phase transitions; fluctuations and transport properties; and coarse-graining approaches and mesoscale models. The course employs case studies from industrial applications of advanced materials to nanotechnology. Several laboratories will give students direct experience with simulations of classical force fields, electronic-structure approaches, molecular dynamics, and Monte Carlo.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5107 (Atomistic Computer Modeling of Materials).
Acknowledgements
Support for this course has come from the National Science Foundation’s Division of Materials Research (grant DMR-0304019) and from the Singapore-MIT Alliance.

Einstein called Galileo the "father of modern physics." This media-rich essay from the NOVA Web site looks at Galileo's quest to understand the mathematics of motion.

This course covers concepts of computation used in analysis of engineering systems. It includes the following topics: data structures, relational database representations of engineering data, algorithms for the solution and optimization of engineering system designs (greedy, dynamic programming, branch and bound, graph algorithms, nonlinear optimization), and introduction to complexity analysis. Object-oriented, efficient implementations of algorithms are emphasized.

We conclude out look at telecommunications and computer networks. We also give our list of additional resources to check out.

Links from Video:
-Wifi Analyzer https://play.google.com/store/apps/details?id=com.farproc.wifi.analyzer
-Cisco Networking Academy https://www.netacad.com/
-Monoprice http://www.monoprice.com

Students learn about the human body's system components, specifically its sensory systems, nervous system and brain, while comparing them to robot system components, such as sensors and computers. The unit's life sciences-to-engineering comparison is accomplished through three lessons and five activities. The important framework of "stimulus-sensor-coordinator-effector-response" is introduced to show how it improves our understanding the cause-effect relationships of both systems. This framework reinforces the theme of the human body as a system from the perspective of an engineer. This unit is the second of a series, intended to follow the Humans Are Like Robots unit.

This course will assess the relationships among sequence, structure, and function in complex biological networks as well as progress in realistic modeling of quantitative, comprehensive, functional genomics analyses. Exercises will include algorithmic, statistical, database, and simulation approaches and practical applications to medicine, biotechnology, drug discovery, and genetic engineering. Future opportunities and current limitations will be critically addressed. In addition to the regular lecture sessions, supplementary sections are scheduled to address issues related to Perl, Mathematica and biology.

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Video 4 in our Introduction to the CompTIA A+ Certification Exam looks at the tools the computer technician should have in their bag in order to get the job done. We look at hardware tools for computer repair and software tools for computer repair.

Syllabus for the course "CSC 59970: Intro to Data Science" delivered at the City College of New York in Fall 2018 by Grant Long as part of the Tech-in-Residence Corps program.

Manipulate light by building pinhole cameras, and by investigating the effects that lenses and prisms have on light.

Lecture #4 for the course: CSCI 49378: Intro to Distributed Systems and Cloud Computing - "Distributed File Systems". Delivered at Hunter College in Spring 2020 by Bonan Liu as part of the Tech-in-Residence Corps program.

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

Contemporary methods of computational cognitive modeling have recently been criticized by Addyman and French (2012) on the grounds that they have not kept up with developments in computer technology and human–computer interaction. They present a manifesto for change according to which, it is argued, modelers should devote more effort to making their models accessible, both to non-modelers (with an appropriate easy-to-use user interface) and modelers alike. We agree that models, like data, should be freely available according to the normal standards of science, but caution against confusing implementations with specifications. Models may embody theories, but they generally also include implementation assumptions. Cognitive modeling methodology needs to be sensitive to this. We argue that specification, replication and experimentation are methodological approaches that can address this issue.

This course is the second half of the intensive survey of brain and behavioral studies for first-year graduate students in the Brain and Cognitive Sciences curriculum. Each module of this core course involves a series of overview lectures by leading researchers in the field. By offering a thorough introduction to the current state of the discipline while emphasizing critical thinking, the course aims to prepare students as cognitive scientists.
Topics include: perception, attention, working memory, recognition and recall, language, and other issues in cognitive science. Topics are covered from the neural, behavioral and computational perspectives.

This section will provide an introduction to cloud computing, its benefits, and the different types of cloud services (IaaS, PaaS, and SaaS). We will also discuss the different deployment models (public, private, hybrid) and the key players in the cloud computing market.

An advanced seminar on issues of current interest in human and machine vision. Topics vary from year to year. This year, the class will involve studying the perception of materials. Participants discuss current literature as well as their ongoing research. Topics are tackled from multiple standpoints, including optics, psychophysics, computer graphics and computer vision.