In this 25-day module of Grade 4, students extend their work with whole numbers.  They begin with large numbers using familiar units (hundreds and thousands) and develop their understanding of millions by building knowledge of the pattern of times ten in the base ten system on the place value chart (4.NBT.1).  They recognize that each sequence of three digits is read as hundreds, tens, and ones followed by the naming of the corresponding base thousand unit (thousand, million, billion).

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.

Building on the programming basics learned so far in the unit, students next learn how to program using sensors rather than by specifying exact durations. They start with an examination of algorithms and move to an understanding of conditional commands (until, then), which require the use of wait blocks. Working with the LEGO MINDSTORMS(TM) NXT robots and software, they learn about wait blocks and how to use them in conjunction with move blocks set with unlimited duration. To help with comprehension and prepare them for the associated activity programming challenges, volunteer students act out a maze demo and student groups conclude by programming LEGO robots to navigate a simple maze using wait block programming. A PowerPoint® presentation, a worksheet and pre/post quizzes are provided.

This course is an introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. It emphasizes the relationship between algorithms and programming and introduces basic performance measures and analysis techniques for these problems.

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5503 (Analysis and Design of Algorithms).

This book acknowledges our changing information landscape, covering key concepts in information literacy to support a research process with intention. We start by critically examining the online environment many of us already engage with every day, looking at algorithms, the attention economy, information disorder and cynicism, information hygiene, and fact-checking. We then move into an exploration of information source types, meaningful research topics, keyword choices, effective search strategies, library resources, Web search considerations, the ethical use of information, and citation.

This is an introductory course on computational thinking. We use the Julia programming language to approach real-world problems in varied areas, applying data analysis and computational and mathematical modeling. In this class you will learn computer science, software, algorithms, applications, and mathematics as an integrated whole. Topics include image analysis, particle dynamics and ray tracing, epidemic propagation, and climate modeling.

Dear student! You are starting to learn about computation and its purpose. This course covers the same materials as an introductory class for undergraduate computer science majors. Its curriculum, which includes software, hardware and algorithms, resembles that of a one- or two-semester first-year college course or the high school Advanced Placement (AP) Computer Science. It does not require a formal computer science background.

This course is a continuation of the first-semester course titled Introduction to Computer Science I. It will introduce the student to a number of more advanced Computer Science topics, laying a strong foundation for future academic study in the discipline. The student will begin with a comparison between Java--the programming language utilized last semester--and C++, another popular, industry-standard programming language. The student will then discuss the fundamental building blocks of Object-Oriented Programming, reviewing what they have learned learned last semester and familiarizing themselves with some more advanced programming concepts. The remaining course units will be devoted to various advanced topics, including the Standard Template Library, Exceptions, Recursion, Searching and Sorting, and Template Classes. By the end of the class, the student will have a solid understanding of Java and C++ programming, as well as a familiarity with the major issues that programmers routinely address in a professional setting. Upon successful completion of this course, the student will be able to: Demonstrate an understanding of the concepts of Java and C++ and how they are used in Object-Oriented Programming; Demonstrate an understanding of the history and development of Object-Oriented Programming; Explain the importance of the C++ Standard Template Library and how basic components are used; Demonstrate a basic understanding of the importance of run-time analysis in programming; Demonstrate an understanding of important sorting and search routines in programming; Demonstrate an understanding of the generic usage of templates in programming for C++ and Java; Compare and contrast the features of Java and C++. (Computer Science 102; See also: Mathematics 303)

This is MIT’s introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), and we’ll try to explain everything else along the way! Experience in Python is helpful but not necessary.

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.
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.

Student groups use the Java programming language to implement the algorithms for optical character recognition (OCR) that they developed in the associated lesson. They use different Java classes (provided) to test and refine their algorithms. The ultimate goal is to produce computer code that recognizes a digit on a scoreboard. Through this activity, students experience a very small part of what software engineers go through to create robust OCR methods. This software design lesson/activity set is designed to be part of a Java programming class.

In this blog post and the included lesson plan, Graham Fletcher and Joe Schwartz explore thinking conceptually about the standard division algorithm. In his lesson, Joe Schwartz scaffolds long division using tape diagrams.

How does backpacking help you as an aerospace engineer? Find out in this edition of My Path.

(Nota: Esta es una traducción de un recurso educativo abierto creado por el Departamento de Educación del Estado de Nueva York (NYSED) como parte del proyecto "EngageNY" en 2013. Aunque el recurso real fue traducido por personas, la siguiente descripción se tradujo del inglés original usando Google Translate para ayudar a los usuarios potenciales a decidir si se adapta a sus necesidades y puede contener errores gramaticales o lingüísticos. La descripción original en inglés también se proporciona a continuación.)

En este módulo de 25 días de grado 4, los estudiantes extienden su trabajo con números enteros. Comienzan con grandes números utilizando unidades familiares (cientos y miles) y desarrollan su comprensión de millones al desarrollar el conocimiento del patrón de tiempos diez en el sistema Base Ten en la tabla de valor del lugar (4.nbt.1). Reconocen que cada secuencia de tres dígitos se lee como cientos, decenas y, seguidas de la denominación de la base correspondiente, mil unidad (mil, millones, mil millones).

Encuentre el resto de los recursos matemáticos de Engageny en https://archive.org/details/engageny-mathematics.

English Description:
In this 25-day module of Grade 4, students extend their work with whole numbers.  They begin with large numbers using familiar units (hundreds and thousands) and develop their understanding of millions by building knowledge of the pattern of times ten in the base ten system on the place value chart (4.NBT.1).  They recognize that each sequence of three digits is read as hundreds, tens, and ones followed by the naming of the corresponding base thousand unit (thousand, million, billion).

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.

Using new knowledge acquired in the associated lesson, students program LEGO MINDSTORMS(TM) NXT robots to go through a maze using movement blocks. The maze is created on the classroom floor with cardboard boxes as its walls. Student pairs follow the steps of the engineering design process to brainstorm, design and test programs to success. Through this activity, students understand how to create and test a basic program. A PowerPoint® presentation, pre/post quizzes and worksheet are provided.

Complément d’exercices théoriques et pratiques en complement du livre Algorithms, 4th Edition de Robert Sedgewick and Kevin Wayne pour une classe inversée.

This course introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance. Includes a team project in which students select and solve a problem in practice.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Microbiome sequencing data are very complex. In order to simplify analyses, researchers often perform unsupervised clustering to identify naturally occurring clusters and then investigate the clusters’ associations with various characteristics of interest. However, clustering performance and related conclusions can vary depending on the algorithm or beta diversity metric used. To improve microbiome analysis methods, a new study tested the performance of several metrics on four datasets with well-separated groups and a clinical dataset with less-clear group separation. None of the metrics was universally superior, but certain metrics underperformed under certain conditions. For example, the Bray-Curtis metric performed poorly in a dataset with rare high-abundance OTUs (groups of related bacteria), while the unweighted UniFrac metric performed poorly in a dataset with prevalent low-abundance OTUs..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.