This is a textbook for first year Computer Science. Algorithms and Data Structures With Applications to Graphics and Geometry.
This course includes materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics, which will introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence. The material is introductory; the readings cite many resources outside those assigned in this course, and students are encouraged to explore these resources to pursue topics of interest. Upon successful completion of this course, the student will be able to: Describe the major applications, topics, and research areas of artificial intelligence (AI), including search, machine learning, knowledge representation and inference, natural language processing, vision, and robotics; Apply basic techniques of AI in computational solutions to problems; Discuss the role of AI research areas in growing the understanding of human intelligence; Identify the boundaries of the capabilities of current AI systems. (Computer Science 405)
Join me for a hands-on ride through the fundamentals of electronics and acoustics and the process of loudspeaker design and construction. We will learn about the engineering and art involved throughout music/movie recording and playback, the design and application of everything from microphones to DACs, amplifiers, and speakers. With the aid of computer assisted audio measuring equipment at the MIT Edgerton Center, we will analyze the frequency response and distortion of speaker drivers, and understand their effect on what we hear. Then we design our own speakers—driver selection, crossover networks, and enclosure design—and build them in class!
This textbook was written for a community college introductory course in spreadsheets utilizing Microsoft Excel. While the figures shown utilize Excel 2016, the textbook was written to be applicable to other versions of Excel as well. The book introduces new users to the basics of spreadsheets and is appropriate for students in any major who have not used Excel before.
One of the fundamental computer science concepts is that everything we do on a computer is really just bits turning on and off. Even though this sounds simple, it can be a concept that is hard to grasp. This activity brings the binary concept to life through math and the creation of binary bracelets.
Blender 3D: Noob to Pro is a product of shared effort by numerous team members and anonymous editors. Its purpose is to teach people how to create three-dimensional computer graphics using Blender, a free software application. This book is intended to be used in conjunction with other on-line resources that complement it.
A digital checklist board that you can use in any content area! This is a project management resource. The directions are geared towards a book report, however, you could adjust them for any project where the students need to stay organized
MIT Lincoln Laboratory offers this 3-week course in the design, fabrication, and test of a laptop-based radar sensor capable of measuring Doppler, range, and forming synthetic aperture radar (SAR) images. You do not have to be a radar engineer but it helps if you are interested in any of the following; electronics, amateur radio, physics, or electromagnetics.
This video module presents an introduction to cryptography - the method of sending messages in such a way that only the intended recipients can understand them. In this very interactive lesson, students will build three different devices for cryptography and will learn how to encrypt and decrypt messages. There are no prerequisites for this lesson, and it has intentionally been designed in a way that can be adapted to many audiences. It is fully appropriate in a high school level math or computer science class where the teacher can use it to motivate probability/statistics or programming exercises. nteractive lesson, students will learn to build the cryptography devices and will learn how to send and ''crack'' secret messages.
C is the most commonly used programming language for writing operating systems. The first operating system written in C is Unix. Later operating systems like GNU/Linux were all written in C. Not only is C the language of operating systems, it is the precursor and inspiration for almost all of the most popular high-level languages available today. In fact, Perl, PHP, Python and Ruby are all written in C. By way of analogy, let's say that you were going to be learning Spanish, Italian, French, or Portuguese. Do you think knowing Latin would be helpful? Just as Latin was the basis of all of those languages, knowing C will enable you to understand and appreciate an entire family of programming languages built upon the traditions of C. Knowledge of C enables freedom.
Although C# is derived from the C programming language, it introduces some unique and powerful features, such as delegates (which can be viewed as type-safe function pointers) and lambda expressions which introduce elements of functional programming languages, as well as a simpler single class inheritance model (than C++) and, for those of you with experience in "C-like" languages, a very familiar syntax that may help beginners become proficient faster than its predecessors. Similar to Java, it is object-oriented, comes with an extensive class library, and supports exception handling, multiple types of polymorphism, and separation of interfaces from implementations. Those features, combined with its powerful development tools, multi-platform support, and generics, make C# a good choice for many types of software development projects: rapid application development projects, projects implemented by individuals or large or small teams, Internet applications, and projects with strict reliability requirements. Testing frameworks such as NUnit make C# amenable to test-driven development and thus a good language for use with Extreme Programming (XP). Its strong typing helps to prevent many programming errors that are common in weakly typed languages.
In this lesson, students go further into the collection and interpretation of data, including cleaning and visualizing data. Students first look at the how presenting data in different ways can help people to understand it better, and they then create visualizations of their own data. Using a the results of a preferred pizza topping survey, students must decide what to do with data that does not easily fit into the visualization scheme that they have chosen. Finally, students look at which parts of this process can be automated by a computer and which need a human to make decisions.
In this lesson students get practice making decisions with data based on some problems designed to be familiar to middle school students. Students work in groups discussing how they would use the data presented to make a decision before the class discusses their final choices. Not all questions have right answers and in some cases students can and should decide that they should collect more data. The lesson concludes with a discussion of how different people could draw different conclusions from the same data, or how collecting different data might have affected the decisions they made.
Students begin the lesson by looking at a cake preference survey that allows respondents to specify both a cake and an icing flavor. They discuss how knowing the relationship between cake and icing preference helps them better decide which combination to recommend. They are then introduced to cross tabulation, which allows them to graph relationships to different preferences. They use this technique to find relationships in a preference survey, then brainstorm the different types of problems that this process could help solve.
In this lesson students look at a simple example of how a computer could be used to complete the decision making step of the data problem solving process. Students are given the task of creating an algorithm that could suggest a vacation spot. Students then create rules, or an algorithm, that a computer could use to make this decision automatically. Students share their rules and what choices their rules would make with the class data. They then use their rules on data from their classmates to test whether their rules would make the same decision that a person would. The lesson concludes with a discussion about the benefits and drawbacks of using computers to automate the data problem solving process.
To conclude this unit, students design a recommendation engine based on data that they collect and analyze from their classmates. After looking at an example of a recommendation app, students follow a project guide to complete this multi-day activity. In the first several steps, students choose what choice they want to help the user to make, what data they need to give the recommendation, create a survey, and collect information about their classmates' choices. They then interpret the data and use what they have learned to create the recommendation algorithm. Last, they use their algorithms to make recommendations to a few classmates. Students perform a peer review and make any necessary updates to their projects before preparing a presentation to the class.
In this lesson, students look at how data is collected and used by organizations to solve problems in the real world. The lesson begins with a quick review of the data problem solving process they explored in the last lesson. Then students are presented three scenarios that could be solved using data and brainstorm the types of data they would want to solve them and how they could collect the data. Each problem is designed to reflect a real-world service that exists. After brainstorming, students watch a video about a real-world service and record notes about what data is collected by the real-world service and how it is used. At the end of the lesson, students record whether data was provided actively by a user, was recorded passively, or is collected by sensors.
In the first lesson of the data unit, students get an overview of what data is and how it is used to solve problems. Students start off with a brief discussion to come to a common understanding of data. They then split into groups and use a data set to make a series of meal recommendations for people with various criteria. Each group has the choices of meal represented in a different way (pictures, recipes, menu, nutrition) that gives an advantage for one of the recommendations. Afterwards, groups compare their responses and discuss how the different representations of the meal data affected how the students were able to solve the different problems.
In this lesson students create their own system for representing information. They begin by brainstorming all the different systems they already use to represent yes-no responses. They then work in small groups to create a system that can represent any letter in the alphabet using only a single stack of cards. The cards used have one of 6 different possible drawings (6 animals, 6 colors, etc.) and so to represent the entire alphabet students will need to use patterns of multiple cards to represent each letter. Students create messages with their systems and exchange with other groups to ensure the system worked as intended. In the wrap-up discussion the class reviews any pros and cons of the different systems. They discuss commonalities between working systems and recognize that there are many possible solutions to this problem and what's important is that everyone use the same arbitrary system to communicate.
In this lesson students learn to use their first binary system for encoding information, the ASCII system for representing letters and other characters. At the beginning of the lesson the teacher introduces the fact that computers must represent information using either "on" or "off". Then students are introduced to the ASCII system for representing text using binary symbols. Students practice using this system before encoding their own message using ASCII. At the end of the lesson a debrief conversation helps synthesize the key learning objectives of the activity.