Coders engage in a multi-day project where they create the introduction to a short story and then pass their project on to be finished by their peers. The purpose of this project is to encourage young coders to communicate and learn from their peers.

Coders review how to use repeat forever blocks with looks and control blocks to create a different simulated environment. The purpose of this project is to continue applying understanding from the previous project to trigger new blocks and block combinations with the green flag.

Coders use a variety of blocks and sprites to create their own interactive diorama on outer space. The purpose of this project is to continue applying understanding from previous projects.

This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference.

Students today develop proficiency with many different algorithms for multiplication. This approach insures that each student will find a method that works effectively for him/her. Teachers model the different algorithms and encourage students to use and practice each method before selecting a favorite.

This course is a comprehensive introduction to control system synthesis in which the digital computer plays a major role, reinforced with hands-on laboratory experience. The course covers elements of real-time computer architecture; input-output interfaces and data converters; analysis and synthesis of sampled-data control systems using classical and modern (state-space) methods; analysis of trade-offs in control algorithms for computation speed and quantization effects. Laboratory projects emphasize practical digital servo interfacing and implementation problems with timing, noise, and nonlinear devices.

This course is an introduction to the theory and application of large-scale dynamic programming. Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks.

This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.

Students explore the concept of optical character recognition (OCR) in a problem-solving environment. They research OCR and OCR techniques and then apply those methods to the design challenge by developing algorithms capable of correctly "reading" a number on a typical high school sports scoreboard. Students use the structure of the engineering design process to guide them to develop successful algorithms. In the associated activity, student groups implement, test and revise their algorithms. This software design lesson/activity set is designed to be part of a Java programming class.

Testing is critical to any design, whether the creation of new software or a bridge across a wide river. Despite risking the quality of the design, the testing stage is often hurried in order to get products to market. In this lesson, students focus on the testing phase of the software/systems design process. They start by exploring existing examples of program testing using the CodingBat website, which contains a series of problems and challenges that students solve using the Java programming language. Working in teams, students practice writing test cases for other groups' code, and then write test cases for a program before writing the program itself.

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:

"Our microbiome has profound impacts on our health, and technological advances have allowed for ever-growing pools of data from microbiome-wide studies. This means that our microbiome could be used to estimate disease risk, but the nature of the data makes the endeavor difficult. Similar issues with using genetics to predict disease risk led to the development of the polygenic risk score. Motivated by the success of that framework, a team of researchers recently developed the microbial risk score (MRS). MRS summarizes the complex microbial profile by first identifying a sub- community consisting of disease-associated microbial taxa and then integrating those microbial taxa into a continuous score based on the alpha diversity of the identified sub-community. MRS can be easily integrated with the other risk scores built upon metatranscriptomics, host genetics, or host transcriptomics, making it useful for 'multi-omics' approaches as well..."

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

This course focuses on the algorithms for analyzing and designing geometric foldings. Topics include reconfiguration of foldable structures, linkages made from one-dimensional rods connected by hinges, folding two-dimensional paper (origami), and unfolding and folding three-dimensional polyhedra. Applications to architecture, robotics, manufacturing, and biology are also covered in this course.
Acknowledgments
Thanks to videographers Martin Demaine and Jayson Lynch.

Kevin Slavin argues that we're living in a world designed for -- and increasingly controlled by -- algorithms. In this riveting talk from TEDGlobal, he shows how these complex computer programs determine: espionage tactics, stock prices, movie scripts, and architecture. And he warns that we are writing code we can't understand, with implications we can't control. A quiz, thought provoking question, and links for further study are provided to create a lesson around the 15-minute video. Educators may use the platform to easily "Flip" or create their own lesson for use with their students of any age or level.

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.

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.

This course begins with an introduction to the theory of computability, then proceeds to a detailed study of its most illustrious result: Kurt Gödel's theorem that, for any system of true arithmetical statements we might propose as an axiomatic basis for proving truths of arithmetic, there will be some arithmetical statements that we can recognize as true even though they don't follow from the system of axioms. In my opinion, which is widely shared, this is the most important single result in the entire history of logic, important not only on its own right but for the many applications of the technique by which it's proved. We'll discuss some of these applications, among them: Church's theorem that there is no algorithm for deciding when a formula is valid in the predicate calculus; Tarski's theorem that the set of true sentence of a language isn't definable within that language; and Gödel's second incompleteness theorem, which says that no consistent system of axioms can prove its own consistency.

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.

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 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.