This subject offers an interactive introduction to discrete mathematics oriented toward computer science and engineering. The subject coverage divides roughly into thirds:

Fundamental concepts of mathematics: Definitions, proofs, sets, functions, relations.
Discrete structures: graphs, state machines, modular arithmetic, counting.
Discrete probability theory.

On completion of 6.042J, students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.
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.

An introduction to computational theories of human cognition. Emphasizes questions of inductive learning and inference, and the representation of knowledge. Project required for graduate credit. This class is suitable for intermediate to advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.

While this version of CS210-text has references that may be specific to University of Oregon, we invite instructors at other Oregon colleges and universities to fork and customize it to their needs.

This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of “computer science beyond computers”: that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. Beginning in antiquity—with Euclid’s algorithm and other ancient examples of computational thinking—the course will progress rapidly through propositional logic, Turing machines and computability, finite automata, Gödel’s theorems, efficient algorithms and reducibility, NP-completeness, the P versus NP problem, decision trees and other concrete computational models, the power of randomness, cryptography and one-way functions, computational theories of learning, interactive proofs, and quantum computing and the physical limits of computation. Class participation is essential, as the class will include discussion and debate about the implications of many of these ideas.

Today we're going to talk about the birth of personal computing. Up until the early 1970s components were just too expensive, or underpowered, for making a useful computer for an individual, but this would begin to change with the introduction of the Altair 8800 in 1975. In the years that follow, we'll see the founding of Microsoft and Apple and the creation of the 1977 Trinity: The Apple II, Tandy TRS-80, and Commodore PET 2001. These new consumer oriented computers would become a huge hit, but arguably the biggest success of the era came with the release of the IBM PC in 1981. IBM completely changed the industry as its "IBM compatible" open architecture consolidated most of the industry except for, notably, Apple. Apple chose a closed architecture forming the basis of the Mac Vs PC debate that rages today. But in 1984, when Apple was losing marketshare fast it looked for a way to offer a new user experience like none other - which we'll discuss next week.

This open text in progress is designed for use in CS 210 at University of Oregon and in CS 161 at other Oregon colleges and universities.

In the digital tapestry of the 21st century, education, particularly in the realms of Computer Applications Technology (CAT) and Information Technology (IT), stands at the forefront of innovation and transformation. It is with immense pride and enthusiasm that we present this pioneering Open Educational Resource (OER) Textbook, a testament to the collaborative spirit and intellectual rigor of a group of distinguished postgraduate students from North-West University, South Africa: L. Van der Walt, B. Molokwane and N. Mbele. Under the editorship of Dr. C. Bosch, this textbook emerges as a beacon of knowledge, co-creation, and dissemination, meticulously crafted to serve the vibrant community of computer science educators.Embarking on a journey through the rich landscapes of learning theories in CAT and IT education, this textbook unveils the multifaceted dimensions of teaching and learning strategies that resonate with the demands of contemporary education. It meticulously navigates through the intricacies of effective pedagogical approaches, ensuring that educators are well-equipped to foster environments where learning is not just absorbed but experienced and enacted.  

Syllabus for the course "CSC 21700 - 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.

Project Assignment for the course "CSC 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.

Effective and relevant computer science education is essential to achieving our vision that “every student is ready for college, career, and life.” While attention to computer science education has increased in recent years, a lack of awareness about its content and potential impact is widespread. The Washington State Computer Science K–12 Learning Standards are designed to enhance teacher understanding and improve student learning so that students are better equipped for college, career, and life.

Washington is committed to implementing high-quality computer science instruction to:

* Increase the opportunity for all students to gain knowledge of computer science.
* Introduce the fundamental concepts and applications of computer science to all students, beginning at the elementary school level.
* Make computer science at the secondary level accessible, worthy of a computer science credit, and/or equivalent to math and science courses as a required graduation credit (see Level 3B of computer science standards).
* Offer additional secondary-level computer science instruction that allows interested students to study facets of computer science in depth and prepare them for entry into a career or college.

Learning standards describe what students need to know and be able to do. Standards are worded broadly to allow flexibility at the district, building, and classroom levels.

Selection of suggested supplemental Computer Science Resources by the Washington Office of Superintendent of Public Instruction. These resources were carefully chosen for their alignment to Washington State Learning Standards and direct experience with effective implementation with students. 

Today we're going to talk about a fundamental part of all modern computers. The thing that basically everything else uses - the Arithmetic and Logic Unit (or the ALU). The ALU may not have to most exciting name, but it is the mathematical brain of a computer and is responsible for all the calculations your computer does! And it's actually not that complicated. So today we're going to use the binary and logic gates we learned in previous episodes to build one from scratch, and then we'll use our newly minted ALU when we construct the heart of a computer, the CPU, in episode 7.

*CORRECTION*

This course covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.

Exploring Computer Science is a yearlong course developed around a framework of both computer science content and computational practice. Assignments and instruction are contextualized to be socially relevant and meaningful for diverse students. Units utilize a variety of tools/platforms and culminate with final projects around Human-Computer Interaction, Problem Solving, Web Design (HTML, CSS), Programming (Scratch, Edware), Computing & Data Analysis, and Robotics. ECS is recognized nationally as a preparatory course for AP Computer Science Principles. Watch this video and view this fact sheet for more information.

This course covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.

This textbook covers the traditional introductory Computer Science I topics but takes a unique approach. Topics are covered in a language-agnostic manner in the first part with supplemental parts that cover the same concepts in a specific language. The current version covers C, Java, and PHP. This textbook as been used in several Computer Science I sections over multiple years at the University of Nebraska-Lincoln.

This is an introdutory lesson to computer science combinded with a 3-part project, students will prepare for programming by practicing the problem-solving steps. They will select a problem that they are dealing with at home, at school, or a problem in the community. They will then research and gather data to help them find a step by step plan to solve the problem.      Lesson Includes: Activity, Pre-Post Survey, 3-part project and rubric 

Among the top challenges of reproducible computational science are: (1) creation, curation, usage and publication of research software; (2) acceptance, adoption and standardization of open-science practices; (3) misalignment with academic incentive structures and institutional processes for career progression. I will address here mainly the first two, proposing a praxis of reproducible computational science.

This is an introductory lesson introducing what computer science is and what some of the topics are that we will cover in their Intro to CS and Web Design Course.