For this 3-part project, students will practice using the problem-solving steps by …
For this 3-part project, students will practice using the problem-solving steps by pretending to help a family member or friend who has asked them to give a recommendation of which computer to buy.
Lecture for the course "CS 217 – Probability and Statistics for Computer …
Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.
The purpose of this course is to cultivate an understanding of modern …
The purpose of this course is to cultivate an understanding of modern computing technology through an in-depth study of the interface between hardware and software. The student will study the history of modern computing technology before learning about modern computer architecture, then the recent switch from sequential processing to parallel processing. Upon completion of this course, students will be able to: identify important advances that have taken place in the history of modern computing and discuss some of the latest trends in computing industry; explain how programs written in high-level programming language, such as C or Java, can be translated into the language of the hardware; describe the interface between hardware and software and explain how software instructs hardware to accomplish desired functions; demonstrate an understanding of the process of carrying out sequential logic design; demonstrate an understanding of computer arithmetic hardware blocks and floating point representation; explain how a hardware programming language is executed on hardware and how hardware and software design affect performance; demonstrate an understanding of the factors that determine the performance of a program; demonstrate an understanding of the techniques that designers use to improve the performance of programs running on hardware; demonstrate an understanding of the importance of memory hierarchy in computer design and explain how memory design impacts overall hardware performance; demonstrate an understanding of storage and I/O devices, their performance measurement, and redundant array of inexpensive disks (more commonly referred to by the acronym RAID) technology; list the reasons for and the consequences of the recent switch from sequential processing to parallel processing in hardware manufacture and explain the basics of parallel programming. (Computer Science 301)
Computer Histories is an introductory course on the history of computing that …
Computer Histories is an introductory course on the history of computing that explores the questions 1) What is the history of computing? 2) What is the future of computing? and 3) What lessons can we learn from computing's past that will help guide us in determining computing's future?
This course emphasizes computability and computational complexity theory. Topics include regular and …
This course emphasizes computability and computational complexity theory. Topics include regular and context-free languages, decidable and undecidable problems, reducibility, recursive function theory, time and space measures on computation, completeness, hierarchy theorems, inherently complex problems, oracles, probabilistic computation, and interactive proof systems.
Practice Final Exam for the course "CS 217 – Probability and Statistics …
Practice Final Exam for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.
We will discuss numerous research problems that are related to the internet. …
We will discuss numerous research problems that are related to the internet. Sample topics include: routing algorithms such as BGP, communication protocols such as TCP, algorithms for intelligently selecting a resource in the face of uncertainty, bandwidth sensing tools, load balancing algorithms, streaming protocols, determining the structure of the internet, cost optimization, DNS-related problems, visualization, and large-scale data processing. The seminar is intended for students who are ready to work on challenging research problems. Each lecture will discuss:
methods used today issues and problems formulation of concrete problems potential new lines of research
A modest amount of background information will be provided so that the importance and context of the problems can be understood. No previous study of the internet is required, but experience with algorithms and/or theoretical computer science at the graduate/research level is needed.
Lecture for the course "CS 217 – Probability and Statistics for Computer …
Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.
This course provides a review of linear algebra, including applications to networks, …
This course provides a review of linear algebra, including applications to networks, structures, and estimation, Lagrange multipliers. Also covered are: differential equations of equilibrium; Laplace's equation and potential flow; boundary-value problems; minimum principles and calculus of variations; Fourier series; discrete Fourier transform; convolution; and applications. Note: This course was previously called "Mathematical Methods for Engineers I."
OBJECTIVES:To know what is a computerTo be familiar with the history of …
OBJECTIVES:To know what is a computerTo be familiar with the history of computers.To identify the different types of computers.To identify the hardware components of a computer.
This unit covers five topics concerning the future of computing: trends in …
This unit covers five topics concerning the future of computing: trends in computing, interfaces used to communicate with computer systems, cloud computing, the changing social implications of the use of computer systems, and the ubiquity of computers in our daily lives.
Replication is the cornerstone of a cumulative science. However, new tools and …
Replication is the cornerstone of a cumulative science. However, new tools and technologies, massive amounts of data, interdisciplinary approaches, and the complexity of the questions being asked are complicating replication efforts, as are increased pressures on scientists to advance their research. As full replication of studies on independently collected data is often not feasible, there has recently been a call for reproducible research as an attainable minimum standard for assessing the value of scientific claims. This requires that papers in experimental science describe the results and provide a sufficiently clear protocol to allow successful repetition and extension of analyses based on original data. The importance of replication and reproducibility has recently been exemplified through studies showing that scientific papers commonly leave out experimental details essential for reproduction, studies showing difficulties with replicating published experimental results, an increase in retracted papers, and through a high number of failing clinical trials. This has led to discussions on how individual researchers, institutions, funding bodies, and journals can establish routines that increase transparency and reproducibility. In order to foster such aspects, it has been suggested that the scientific community needs to develop a “culture of reproducibility” for computational science, and to require it for published claims. We want to emphasize that reproducibility is not only a moral responsibility with respect to the scientific field, but that a lack of reproducibility can also be a burden for you as an individual researcher. As an example, a good practice of reproducibility is necessary in order to allow previously developed methodology to be effectively applied on new data, or to allow reuse of code and results for new projects. In other words, good habits of reproducibility may actually turn out to be a time-saver in the longer run. We further note that reproducibility is just as much about the habits that ensure reproducible research as the technologies that can make these processes efficient and realistic. Each of the following ten rules captures a specific aspect of reproducibility, and discusses what is needed in terms of information handling and tracking of procedures. If you are taking a bare-bones approach to bioinformatics analysis, i.e., running various custom scripts from the command line, you will probably need to handle each rule explicitly. If you are instead performing your analyses through an integrated framework (such as GenePattern, Galaxy, LONI pipeline, or Taverna), the system may already provide full or partial support for most of the rules. What is needed on your part is then merely the knowledge of how to exploit these existing possibilities.
Lecture for the course "CS 217 – Probability and Statistics for Computer …
Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.
A 24-page booklet that showcases the exciting ways that scientists are using …
A 24-page booklet that showcases the exciting ways that scientists are using the power of computers to expand our knowledge of biology and medicine. (National Institute of General Medical Sciences)
This course examines computers anthropologically, as artifacts revealing the social orders and …
This course examines computers anthropologically, as artifacts revealing the social orders and cultural practices that create them. Students read classic texts in computer science along with cultural analyses of computing history and contemporary configurations. It explores the history of automata, automation and capitalist manufacturing; cybernetics and WWII operations research; artificial intelligence and gendered subjectivity; robots, cyborgs, and artificial life; creation and commoditization of the personal computer; the growth of the Internet as a military, academic, and commercial project; hackers and gamers; technobodies and virtual sociality. Emphasis is placed on how ideas about gender and other social differences shape labor practices, models of cognition, hacking culture, and social media.
This resource contains several ready made curricula for lower primary students. The …
This resource contains several ready made curricula for lower primary students. The resources are easy to use and contain all of the information a teacher would need to teach the lessons/units.
This course provides an integrated introduction to electrical engineering and computer science, …
This course provides an integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Our primary goal is for you to learn to appreciate and use the fundamental design principles of modularity and abstraction in a variety of contexts from electrical engineering and computer science. Our second goal is to show you that making mathematical models of real systems can help in the design and analysis of those systems. Finally, we have the more typical goals of teaching exciting and important basic material from electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making. Course Format This course has been designed for independent study. It includes all of the materials you will need to understand the concepts covered in this subject. The materials in this course include:
Lecture videos from Spring 2011, taught by Prof. Dennis Freeman Recitation videos, developed for OCW Scholar by teaching assistant Kendra Pugh Course notes Software and design labs Homework assignments and additional exercises Nano-quizzes and exams with solutions
Content Development Leslie Kaelbling Jacob White Harold Abelson Dennis Freeman Tomás Lozano-Pérez Isaac Chuang
The framework was developed by the Kapor Center to bring a focus …
The framework was developed by the Kapor Center to bring a focus to equity in Computer Science Education, specifically around teacher preparation, professional development, curriculum development, and policy-making.
No restrictions on your remixing, redistributing, or making derivative works. Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make derivative works.
Most restrictive license type. Prohibits most uses, sharing, and any changes.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based educators, or other custom arrangements. Go to the resource provider to see their individual restrictions.