This is the third of three lessons that make the connection between programming and algorithms. In this lesson students continue to work with the "Human Machine Language" to get creative designing more algorithms for playing cards. One command is added to the language from the previous lesson (SWAP) that allows positions of cards to change. With the addition of swap the challenge is to design an algorithm that will move the minimum card to the front of the list while keeping the relative order of all the other cards the same. If that is achieved some other Human Machine Language challenges are available.

This collection of 6 games develops place value, operation sense and fluency, estimation, and strategic thinking. It is played on paper with a 6- or 9-sided die (printable pdf game sheets included). Players choose a game grid with empty cells representing the digits of a computation (addition, subtraction, multiplication, or division). They take turns rolling a die and placing the resulting number in a cell on their own grids. The goal is to create a sum/difference/product/quotient that is closest to a target number. Variations include using a decimal point and scoring options. Teachers' Notes include suggestions for introducing the game, discussion questions, support suggestions, and a link to a more challenging extension (Countdown, cataloged separately).

In this activity, students explore number decomposition and the powers of two. They play a number guessing game, and by the presence or absence of the secret number on each of six cards, the number can be found. An applet has the computer play the trick with the learner. The Teachers' Notes page offers rationale, suggestions for implementation, discussion questions, and ideas for support and extension.

This is a computer science lesson plan created by educators in the South Sanpete School District. Students will learn what algorithms are by walking in a sequence from one item to another. They will write an algorithm using symbols of the sequence from one spot to another. The lesson is designed for third grade and includes modifications for grade 4.

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.

In this undergraduate level seminar series topics vary from year to year. Students present and discuss the subject matter, and are provided with instruction and practice in written and oral communication. Some experience with proofs required. The topic for fall 2008: Computational algebra and algebraic geometry.

This video lesson will introduce students to algorithmic thinking through the use of a popular field in graph theory—social networking. Specifically, by acting as nodes in a graph (i.e. people in a social network), the students will experientially gain an understanding of graph theory terminology and distance in a graph (i.e. number of introductions required to meet a target person). Once the idea of distance in a graph has been built, the students will discover Dijkstra's Algorithm. The lesson should take approximately 90 minutes and can be comfortably partitioned across two class sessions if necessary (see the note in the accompanying Teacher Guide). There are no special supplies needed for this class and all necessary hand-outs can be downloaded from this website.