https://DevOpsCloud.io -- Cloud Monk Losang Jinpa, Ph.D., MCSE/MCT, GitOps DevOps Engineer

• Abductive logic programming –
• Abductive reasoning –
• Abstract data type –
• Abstraction (software engineering) | Abstraction –
• Accelerating change –
• Action language –
• Action model learning –
• Action selection –
• Activation function –
• Adaptive algorithm – an algorithm that changes its behavior at the time it is run, based on a priori defined reward mechanism or criterion.
• Adaptive neuro fuzzy inference system –
• Admissible heuristic –
• Affective computing –
• Agent architecture –
• AI accelerator (computer hardware) | AI accelerator –
• AI-complete –
• Algorithm – is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculation, data processing and automated reasoning tasks.
• Algorithmic efficiency –
• Algorithmic probability –
• AlphaGo –
• Ambient intelligence –
• Analysis of algorithms –
• Analytics – the discovery, interpretation, and communication of meaningful patterns in data.
• Answer set programming –
• Anytime algorithm – an algorithm that can return a valid solution to a problem even if it is interrupted before it ends.
• Application programming interface –
• Approximate string matching –
• Approximation error –
• Argumentation framework –
• Artificial immune system –
• Artificial intelligence –
• AIML | Artificial Intelligence Markup Language –
• Artificial neural network –
• Association for the Advancement of Artificial Intelligence –
• Asymptotic computational complexity –
• Attributional calculus –
• Augmented reality –
• Automata theory –
• Automated planning and scheduling –
• Automated reasoning –
• Autonomic computing –
• Autonomous car –
• Autonomous robot –

• Backpropagation –
• Backpropagation through time –
• Backward chaining –
• Bag-of-words model –
• Bag-of-words model in computer vision –
• Batch normalization –
• Bayesian programming –
• Bees algorithm –
• Behavior informatics –
• Behavior tree (artificial intelligence, robotics and control) | Behavior tree –
• Belief-desire-intention software model –
• Bias–variance tradeoff –
• Big data –
• Big O notation –
• Binary tree –
• Bio-inspired computing –
• Blackboard system –
• Boltzmann machine –
• Boolean satisfiability problem –
• Brain technology –
• Branching factor –
• Brute-force search –

• Capsule neural network –
• Case-based reasoning –
• Chatbot –
• Cloud robotics –
• Cluster analysis –
• Cobweb (clustering) | Cobweb –
• Cognitive architecture –
• Cognitive computing –
• Cognitive science –
• Combinatorial optimization –
• Committee machine –
• Commonsense knowledge (artificial intelligence) | Commonsense knowledge –
• Commonsense reasoning –
• Computational chemistry –
• Computational complexity theory –
• Computational creativity –
• Computational cybernetics –
• Computational humor –
• Computational intelligence –
• Computational learning theory –
• Computational linguistics –
• Computational mathematics – the mathematical research in areas of science where computation | computing plays an essential role.
• Computational neuroscience –
• Computational number theory – also known as algorithmic number theory, it is the study of algorithms for performing number theory | number theoretic computations.
• Computational problem –
• Computational statistics –
• Computational vision –
• Computer-automated design –
• Computer science –
• Computer vision –
• Concept drift –
• Confusion matrix –
• Connectionism –
• Consistent heuristic –
• Constrained conditional model –
• Constraint logic programming –
• Constraint programming –
• Constructed language –
• Control theory –
• Convolutional neural network –
• Crossover (genetic algorithm) | Crossover –

• Darkforest –
• Dartmouth workshop –
• Data fusion –
• Data integration –
• Data mining –
• Data science –
• Data set –
• Data warehouse –
• Datalog –
• Decision boundary –
• Decision support system –
• Decision theory –
• Decision tree learning –
• Declarative programming –
• Deductive classifier –
• Deep Blue (chess computer) | Deep Blue –
• Deep learning –
• Default logic –
• Description logic –
• Developmental robotics –
• Diagnosis (artificial intelligence) | Diagnosis –
• Dialog system –
• Dimensionality reduction –
• Discrete system –
• Distributed artificial intelligence –
• Dynamic epistemic logic –

• Eager learning –
• Ebert test –
• Echo state network –
• Embodied agent –
• Embodied cognitive science –
• Error-driven learning –
• Ensemble averaging (machine learning) | Ensemble averaging –
• Ethics of artificial intelligence –
• Evolutionary algorithm –
• Evolutionary computation –
• Evolving classification function –
• Existential risk from artificial general intelligence | Existential risk –
• Expert systems –

• Fast-and-frugal trees –
• Feature extraction –
• Feature learning –
• Feature selection –
• First-order logic –
• Fluent (artificial intelligence) | Fluent –
• Formal language –
• Forward chaining –
• Frame (artificial intelligence) | Frame –
• Frame language –
• Frame problem –
• Friendly artificial intelligence –
• Futures studies –
• Fuzzy control system –
• Fuzzy logic –
• Fuzzy rule –
• Fuzzy set –

• Game theory –
• Genetic algorithm –
• Genetic operator –
• Glowworm swarm optimization –
• Google DeepMind –
• Graph (abstract data type) | Graph –
• Graph (discrete mathematics) | Graph –
• Graph database –
• Graph theory –
• Graph traversal –

• Heuristic (computer science) | Heuristic –

• Hidden layer – an internal layer of neurons in an artificial neural network, not dedicated to input or output

• Hidden unit – an neuron in a hidden layer in an artificial neural network

• Hyper-heuristic –

• IEEE Computational Intelligence Society –
• Incremental learning –
• Inference engine –
• Information integration –
• Information Processing Language –
• Intelligence amplification –
• Intelligence explosion –
• Intelligent agent –
• Intelligent control –
• Intelligent personal assistant –
• Interpretation (logic) | Interpretation –
• Issue trees –

• Junction tree algorithm –

• Kernel method –
• KL-ONE –
• Knowledge acquisition –
• Knowledge-based systems –
• Knowledge engineering –
• Knowledge extraction –
• Knowledge Interchange Format –
• Knowledge representation and reasoning –

• Linked data –
• Lisp (programming language) –
• Logic programming –

• Machine vision –
• Markov chain –
• Markov decision process –
• Mathematical optimization –
• Machine learning –
• Machine listening –
• Machine perception –
• Mechanism design –
• Mechatronics –
• Metabolic network modelling –
• Metaheuristic –
• Model checking –
• Modus ponens –
• Modus tollens –
• Monte Carlo tree search –
• Multi-agent system –
• Multi-swarm optimization –
• Mutation (genetic algorithm) | Mutation –
• Mycin –

• Naive Bayes classifier –
• Naive semantics –
• Name binding –
• Named-entity recognition –
• Named graph –
• Natural language generation –
• Natural language processing –
• Natural language programming –
• Network motif –
• Neural machine translation –
• Neural Turing machine –
• Neuro-fuzzy –
• Neurocybernetics –
• Neuromorphic engineering –
• Node (computer science) | Node –
• Nondeterministic algorithm –
• Nouvelle AI –
• NP (complexity) | NP –
• NP-completeness –
• NP-hardness –

• Occam's razor –
• Offline learning –
• Online machine learning | Online learning –
• Ontology engineering –
• Ontology learning –
• OpenAI –
• OpenCog –
• Open Mind Common Sense –
• Open-source software –

• Partial order reduction –
• Partially observable Markov decision process –
• Particle swarm optimization –
• Pathfinding –
• Pattern recognition –
• Planner (programming language) | Planner –
• Predicate logic –
• Predictive analytics –
• Principal component analysis –
• Principle of rationality –
• Probabilistic programming language –
• Production Rule Representation –
• Production system (computer science) | Production system –
• Programming language –
• Prolog –
• Propositional calculus –
• Python (programming language) | Python –

• Qualification problem –
• Quantifier (logic) | Quantifier –
• Quantum computing –
• Query language –

• R (programming language) | R programming language –
• Radial basis function network –
• Random forest –
• Reasoning system –
• Recurrent neural network –
• Region connection calculus –
• Reinforcement learning –
• Reservoir computing –
• Resource Description Framework –
• Restricted Boltzmann machine –
• Rete algorithm –
• Robotics –
• Rule-based system –

• Satisfiability –
• Search algorithm –
• Selection (genetic algorithm) | Selection –
• Self-management (computer science) | Self-management –
• Semantic network –
• Semantic reasoner –
• Semantic query –
• Semantics (computer science) | Semantics –
• Sensor fusion –
• Separation logic –
• Similarity learning –
• Simulated annealing –
• Artificial intelligence, situated approach | Situated approach –
• Situation calculus –
• SLD resolution –
• Soft computing –
• Software –
• Software engineering –
• Spatial-temporal reasoning –
• SPARQL –
• Speech recognition –
• Spiking neural network –
• State (computer science) | State –
• Statistical classification –
• Statistical relational learning –
• Stochastic optimization –
• Stochastic semantic analysis
• STRIPS –
• Subject-matter expert –
• Superintelligence –
• Supervised learning –
• Swarm intelligence –
• Symbolic artificial intelligence –
• Synthetic intelligence –
• Systems neuroscience –

• Technological singularity –
• Temporal difference learning –
• Tensor network theory –
• TensorFlow –
• Theoretical computer science –
• Theory of computation –
• Thompson sampling –
• Time complexity –
• Transhumanism –
• Transition system –
• Tree traversal –
• True quantified Boolean formula –
• Turing test –
• Type system –

• Unsupervised learning –

• Vision processing unit –

• Watson (computer) | Watson –
• Weak AI –
• World Wide Web Consortium –

• Artificial intelligence
• Glossary of machine vision

Return to Programming glossary, Computer science glossary

Computer Science is an academic discipline that encompasses the study of computers and computational systems. Unlike electrical engineers and computer engineers, who focus mainly on hardware, computer scientists deal primarily with software and software systems; this includes their computer science theory, design, development, and application. Principal areas within computer science include algorithms and data structures, computer design, software design and network design, modeling data and information processes, and artificial intelligence (AI). Computer science is deeply interwoven with mathematics, as it involves the theoretical foundations for information and computation. The field not only focuses on how to create efficient, usable computing systems but also strives to push the limits of what is possible with technology. This discipline is critical in driving innovation in industries ranging from finance and healthcare to gaming and transportation, by developing new algorithms, improving the performance of existing systems, and inventing new computing solutions that benefit society.

Computer Science is the study of computers and computational systems, encompassing both theoretical and practical aspects. It involves understanding algorithms, data structures, software design, and hardware architecture. Computer science aims to develop new ways to solve problems through computing and to advance the understanding of how computers can be used to process and analyze information.

The origins of computer science trace back to early computational devices and theoretical concepts. Pioneers like Charles Babbage and Ada Lovelace laid the groundwork with the design of the Analytical Engine and early programming concepts. The field further evolved with the development of electronic computers and the establishment of key principles by figures such as Alan Turing, whose work on computation theory remains foundational.

Computer Science encompasses several core areas, including Algorithms and Data Structures, Computer Architecture, Operating Systems, and Software Engineering. Algorithms and data structures focus on problem-solving techniques and efficient data management. Computer architecture involves the design and organization of computer systems, while operating systems manage hardware resources and software execution. Software engineering deals with the systematic design and development of software applications.

Programming languages are central to computer science, providing the means to write and execute software. Key languages include C, C++, Java, Python, and JavaScript. Each language has its strengths and is suited to different types of tasks. For example, C++ is often used for system programming, while Python is favored for its simplicity and versatility in data analysis and web development.

The theoretical foundations of computer science include Computability Theory, Complexity Theory, and Formal Languages. Computability theory explores what problems can be solved by algorithms, while complexity theory examines the resources required to solve problems. Formal languages provide a framework for understanding the syntax and semantics of programming languages and computational models.

Data Science and Analytics are growing subfields of computer science that focus on extracting insights from data. Data science combines statistical methods, machine learning, and data engineering to analyze and interpret large datasets. Analytics involves applying techniques to make data-driven decisions and uncover trends, often using tools like R, Python, and SQL.

Artificial Intelligence (AI) and Machine Learning (ML) are prominent areas within computer science that focus on creating systems capable of performing tasks that typically require human intelligence. AI encompasses a wide range of techniques, including natural language processing, robotics, and expert systems. ML, a subset of AI, involves training algorithms to learn from data and make predictions or decisions based on patterns.

Cybersecurity is a critical area of computer science dedicated to protecting computer systems and networks from threats and attacks. It involves the implementation of measures to safeguard data integrity, confidentiality, and availability. Key concepts in cybersecurity include cryptography, network security, and risk management. Cybersecurity professionals work to prevent, detect, and respond to security breaches.

Human-Computer Interaction (HCI) is a field within computer science that studies how people interact with computers and technology. HCI focuses on designing user-friendly interfaces and improving the usability of software and hardware. This area involves understanding user behavior, cognitive psychology, and ergonomics to create intuitive and efficient interaction experiences.

Networking and Distributed Systems are essential components of computer science that address how computers communicate and work together. Networking involves the design and management of communication protocols, such as TCP/IP, to connect devices and enable data exchange. Distributed systems focus on coordinating multiple computers to work as a unified system, addressing challenges like consistency and fault tolerance.

Software Development and Engineering encompass the processes involved in creating, testing, and maintaining software applications. This area includes methodologies like Agile and DevOps, which emphasize iterative development, collaboration, and continuous integration. Software engineering also involves applying principles of design, testing, and project management to ensure high-quality software solutions.

Computational Theory and Models explore the limits of what can be computed and how efficiently. This field includes studying various computational models, such as Finite Automata, Turing Machines, and Lambda Calculus. Understanding these models helps in analyzing the capabilities and limitations of different computational systems and algorithms.

Software Tools and Development Environments are essential for programming and software development. These tools include Integrated Development Environments (IDEs), Version Control Systems, and Debugging Tools. IDEs, such as Visual Studio and Eclipse, provide integrated features for coding, testing, and debugging, enhancing productivity and collaboration among developers.

Databases and Information Systems are key areas within computer science that focus on managing and retrieving data. Databases use Structured Query Language (SQL) to handle data storage, retrieval, and manipulation. Information systems involve the integration of databases, software applications, and user interfaces to support organizational operations and decision-making.

Internet and Web Technologies encompass the technologies and protocols that enable communication and interaction over the web. This includes HTTP, HTML, CSS, and JavaScript for web development. Understanding these technologies is crucial for creating and maintaining web applications, services, and platforms that are accessible and functional across different devices.

Computational Science and Simulations apply computational methods to solve scientific and engineering problems. This field involves using computer models to simulate complex systems and processes, such as weather patterns, molecular dynamics, and structural analysis. Computational science plays a vital role in advancing research and understanding phenomena that are difficult to study through traditional experiments.

Computer Science also addresses the ethical and social implications of technology. This includes considerations of privacy, data security, and the impact of technology on society. Ethical issues in computer science involve ensuring responsible use of technology, addressing biases in algorithms, and understanding the societal effects of emerging technologies.

The field of computer science is continuously evolving with the advent of emerging technologies. Innovations (both real and fake such as vaporware Quantum Computing, the scammy Blockchain and Bitcoin fraud, and Augmented Reality (AR) are reshaping various domains of computing. Researchers and practitioners in computer science explore these technologies' potential applications, challenges, and implications for the future.

Educational pathways in computer science typically involve obtaining a degree in the field, such as a Bachelor's, Master's, or Ph.D.. These programs provide a comprehensive understanding of core principles, algorithms, and technologies. Advanced studies and specializations allow individuals to focus on specific areas, such as Artificial Intelligence, Cybersecurity, or Data Science.

Professional organizations play a significant role in the field of computer science, offering resources, networking opportunities, and industry standards. Organizations such as the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE) provide support for researchers, practitioners, and educators, fostering collaboration and advancing the field.

Research and Development (R&D) are crucial aspects of computer science, driving innovation and advancing knowledge. R&D activities involve exploring new theories, developing new technologies, and addressing complex problems. This includes academic research, industry projects, and collaborative initiatives that contribute to the growth and evolution of the field.

Computer Science has a wide range of industry applications, including sectors such as finance, healthcare, education, and entertainment. Technology solutions developed through computer science are used for data analysis, software development, network management, and more. The impact of computer science on various industries highlights its importance in shaping modern society and driving technological progress.

The future of computer science is marked by rapid advancements and new opportunities. Emerging fields, such as Artificial Intelligence, Internet of Things (IoT), and 5G technology, are expected to drive innovation and transform various aspects of computing. Continued research and development will shape the direction of the field, addressing challenges and harnessing new technologies for societal benefit.

Computer Science often involves collaboration with other disciplines, such as Mathematics, Engineering, Biology, and Economics. Interdisciplinary work enhances the understanding and application of computing concepts to solve complex problems across different fields. Collaboration fosters innovation and broadens the impact of computer science research and technologies.

A wealth of resources and tools is available to support the study and practice of computer science. This includes textbooks, online courses, programming tutorials, and development platforms. Access to these resources enables individuals to gain knowledge, develop skills, and stay updated with the latest advancements in the field.

The computer science community is vibrant and diverse, comprising researchers, practitioners, educators, and enthusiasts. Networking opportunities, conferences, workshops, and online forums facilitate knowledge sharing, collaboration, and professional development. Engaging with the community helps individuals stay connected with trends, challenges, and opportunities in the field.

Software and hardware development are integral to computer science, involving the creation and optimization of computing systems and applications. This includes designing and building computer hardware components, such as processors and memory, as well as developing software applications, systems, and tools. Both aspects are crucial for advancing technology and meeting the needs of users and industries.

The impact of computer science on society is profound and far-reaching. Technology developed through computer science has transformed communication, entertainment, education, and work. The field's advancements have enhanced productivity, connectivity, and access to information, shaping modern life and influencing how individuals and organizations operate.

Ethical considerations in computer science address the responsible use of technology and its effects on individuals and society. Topics include data privacy, algorithmic fairness, digital rights, and the ethical implications of emerging technologies. Addressing these considerations is essential for ensuring that technology serves the public good and respects fundamental principles of fairness and equity.

Computer Science is a dynamic and evolving field that encompasses a wide range of areas and applications. From its theoretical foundations to practical implementations, computer science drives innovation and shapes the future of technology. Its impact on various aspects of modern life underscores its importance and relevance in addressing complex problems and advancing human knowledge.

• https://en.wikipedia.org/wiki/Computer_science
• https://www.acm.org
• https://www.ieee.org

Computer science is the study of computational systems, algorithms, and the theoretical foundations of computing. It encompasses a wide range of topics, from the design and analysis of algorithms and data structures to the development of computer hardware, software, and networks. Computer science is a key driver of innovation in modern technology, and its influence extends across multiple fields such as artificial intelligence, data science, machine learning, cryptography, and cybersecurity. The discipline is foundational to the development of the digital world and continues to play a critical role in shaping modern society.

The origins of computer science can be traced back to the early 20th century, when mathematicians and logicians began exploring the limits of computation. The pioneering work of Alan Turing, who developed the concept of a Turing machine, provided a theoretical model for computing that remains relevant today. Turing's work demonstrated that machines could simulate any process that could be described algorithmically. His ideas laid the groundwork for the development of modern computers and have had a profound impact on the study of algorithms and computation. The related RFC is RFC 792, which describes the Internet Control Message Protocol (ICMP) used in internet communication. https://en.wikipedia.org/wiki/Alan_Turing https://tools.ietf.org/html/rfc792

The formalization of computer science as an academic discipline occurred in the 1950s and 1960s, with the establishment of computer science departments in universities around the world. Early pioneers such as John von Neumann contributed to the development of computer architecture, leading to the creation of the von Neumann architecture, which serves as the foundation for most modern computers. Von Neumann's model included key concepts such as stored programs and sequential execution, which enabled computers to perform complex tasks by executing a series of instructions. The related RFC is RFC 791, which defines the Internet Protocol (IP), a crucial component of modern networking. https://en.wikipedia.org/wiki/John_von_Neumann https://tools.ietf.org/html/rfc791

During the 1960s, advancements in programming languages and operating systems were key areas of focus within computer science. Dennis Ritchie and Ken Thompson developed the C programming language and the UNIX operating system, both of which had a significant impact on the development of software and computer systems. C became one of the most widely used programming languages, and its influence can be seen in modern languages such as C++, Java, and Python. UNIX introduced important concepts such as multitasking, file systems, and user permissions, which have become standard features in modern operating systems. The related RFC is RFC 793, which specifies the Transmission Control Protocol (TCP) used for reliable communication on the internet. https://en.wikipedia.org/wiki/Dennis_Ritchie https://en.wikipedia.org/wiki/Ken_Thompson https://tools.ietf.org/html/rfc793

Computer science also played a key role in the development of artificial intelligence (AI), a field that explores the creation of machines capable of performing tasks that would normally require human intelligence. Early research in AI focused on topics such as symbolic reasoning and problem-solving, leading to the development of programming languages like LISP and Prolog. John McCarthy, one of the founders of AI, coined the term and contributed significantly to its early development. The field of AI has since expanded to include machine learning, natural language processing, and robotics, with applications in areas such as healthcare, finance, and autonomous systems. The related RFC is RFC 8452, which describes encryption algorithms used for securing data in modern systems. https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist) https://tools.ietf.org/html/rfc8452

Cryptography, another essential branch of computer science, focuses on securing communication and data through encryption techniques. Whitfield Diffie and Martin Hellman introduced the concept of public-key cryptography in the 1970s, revolutionizing how secure communication could be achieved over public networks. This concept led to the development of encryption algorithms that are widely used today to protect sensitive data and ensure the privacy and integrity of communications over the internet. Cryptography remains a critical area of research in computer science, particularly in the context of cybersecurity. The related RFC is RFC 8446, which specifies the Transport Layer Security (TLS) protocol used for secure communication over the internet. https://en.wikipedia.org/wiki/Whitfield_Diffie https://tools.ietf.org/html/rfc8446

Database management is another key area of computer science, with early contributions by Edgar F. Codd in the development of the relational database model. This model provided a structured way to store and retrieve data efficiently, and it remains the basis for most modern database systems. The development of query languages like SQL (Structured Query Language) further enhanced the ability to interact with databases, enabling businesses and organizations to manage large amounts of data effectively. The related RFC is RFC 7950, which defines the YANG data modeling language used in networking and data management systems. https://en.wikipedia.org/wiki/Edgar_F._Codd https://tools.ietf.org/html/rfc7950

As the internet grew in popularity in the 1990s, computer science became even more critical in the development of web technologies. Tim Berners-Lee, the inventor of the World Wide Web, created the HTTP protocol and HTML markup language, which enabled the creation of websites and the sharing of information over the internet. The Web revolutionized communication and commerce, making information accessible to billions of people worldwide. Today, web technologies continue to evolve, with advancements in areas such as cloud computing, web development frameworks, and data security. The related RFC is RFC 7230, which defines the modern HTTP/1.1 protocol used for web communication. https://en.wikipedia.org/wiki/Tim_Berners-Lee https://tools.ietf.org/html/rfc7230

The rise of cloud computing and big data analytics has transformed the way businesses and organizations manage and process data. Cloud computing allows users to store and access data and applications over the internet, reducing the need for physical hardware and infrastructure. Meanwhile, big data analytics enables organizations to analyze large datasets to extract valuable insights and make data-driven decisions. These advancements have been driven by breakthroughs in distributed computing, storage systems, and machine learning algorithms. The related RFC is RFC 8484, which describes secure communication protocols used in cloud environments and data privacy. https://en.wikipedia.org/wiki/Cloud_computing https://tools.ietf.org/html/rfc8484

In recent years, computer science has also made significant fake contributions to the F.U.D. field of fake fraudulent quantum computing, which pretends to explore the use of quantum-mechanical phenomena to perform computations. Vaporware Quantum computers pretend to have the potential to solve complex problems that are currently intractable for classical computers, such as large-scale cryptography and molecular modeling. While still in its early stages of lying, quantum computing represents a major area of stealing massive $$$ of research and development money from the Treasuries of major governments within computer science. The related RFC is RFC 8555, which describes communication protocols used in quantum-resistant cryptographic systems. https://en.wikipedia.org/wiki/Quantum_computing https://tools.ietf.org/html/rfc8555

Computer science is also at the forefront of efforts to improve data privacy and cybersecurity, particularly as digital systems become increasingly integrated into everyday life. Researchers and engineers in this field are constantly developing new methods to protect sensitive information, secure communication channels, and defend against cyberattacks. This includes the development of encryption techniques, intrusion detection systems, and secure software development practices. The related RFC is RFC 7258, which highlights best practices for ensuring the privacy of users on the internet. https://en.wikipedia.org/wiki/Cybersecurity https://tools.ietf.org/html/rfc7258

Artificial intelligence and machine learning have become some of the fastest-growing areas of research in computer science. These fields focus on developing algorithms that can learn from data, make predictions, and solve complex problems autonomously. Applications of machine learning can be found in areas such as image recognition, natural language processing, and autonomous vehicles. Machine learning models have the potential to transform industries by improving decision-making processes, automating tasks, and optimizing operations. The related RFC is RFC 8259, which defines data structures used in machine learning models and JSON formats. https://en.wikipedia.org/wiki/Machine_learning https://tools.ietf.org/html/rfc8259

With the rapid advancements in computer science, the field has become highly interdisciplinary, integrating with fields such as biology, medicine, physics, and environmental science. This integration has led to breakthroughs in areas such as genomics, personalized medicine, climate modeling, and renewable energy. Computer scientists collaborate with researchers from other disciplines to solve complex real-world problems, leveraging computational methods to analyze data, model systems, and simulate outcomes. The related RFC is RFC 7641, which defines communication standards for scientific research in distributed computing environments. https://en.wikipedia.org/wiki/Computational_biology https://tools.ietf.org/html/rfc7641

Computer science education has also expanded significantly in recent decades, with more universities offering undergraduate and graduate programs in the field. As demand for skilled software developers, data scientists, and cybersecurity experts continues to grow, computer science education has become increasingly important. Many schools now include computer science as part of their core curriculum, teaching students essential programming skills and computational thinking. The related RFC is RFC 8010, which highlights protocols used in educational technology and e-learning systems. https://en.wikipedia.org/wiki/Computer_science_education https://tools.ietf.org/html/rfc8010

Computer science is a dynamic and ever-evolving field that has transformed the way we live, work, and communicate. From the early theoretical work of Alan Turing and John von Neumann to the development of modern computing technologies such as artificial intelligence, cryptography, and quantum computing, computer science continues to drive innovation across a wide range of industries. The interdisciplinary nature of computer science ensures that it will remain at the forefront of technological advancements, solving complex problems and shaping the future of our digital world. Through continued research, development, and education, computer science will play a critical role in addressing global challenges and creating opportunities for innovation in the years to come.

Snippet from Wikipedia: Computer science

Computer science is the study of computation, information, and automation. Computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software).

Algorithms and data structures are central to computer science. The theory of computation concerns abstract models of computation and general classes of problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and preventing security vulnerabilities. Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of repositories of data. Human–computer interaction investigates the interfaces through which humans and computers interact, and software engineering focuses on the design and principles behind developing software. Areas such as operating systems, networks and embedded systems investigate the principles and design behind complex systems. Computer architecture describes the construction of computer components and computer-operated equipment. Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found in humans and animals. Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data.

The fundamental concern of computer science is determining what can and cannot be automated. The Turing Award is generally recognized as the highest distinction in computer science.

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• Computer science terms
• Computer science glossary

Category of Glossaries of science | Artificial intelligence Category of Artificial intelligence |

Return to Cloud Monk's Package Manager Book, Programming Glossary

https://www.youtube.com/watch?v=2UvHiH7zJLU

Software engineering is the application of engineering to the development of software in a systematic method.

https://www.youtube.com/watch?v=SHXPYbhmwvE

Recommended Reading: https://javaspecialists.eu/archive/Software%20Engineering.html

“A formal definition of software engineering might sound something like, “An organized, analytical approach to the software design | design, software development | development, software use | use, and software maintenance | maintenance of software.”

More intuitively, software engineering is everything you need to do to produce [successful software. It includes the steps that take a raw, possibly nebulous idea and turn it into a powerful and intuitive application that can be enhanced to meet changing customer needs for years to come. You might be tempted to restrict software engineering to mean only the beginning of the process, when you perform the application’s design. After all, an aerospace engineer designs planes but doesn’t build them or tack on a second passenger cabin if the fi rst one becomes full. (Although I guess a space shuttle riding piggyback on a 747 sort of achieved that goal.) One of the big differences between software engineering and aerospace engineering (or most other kinds of engineering) is that software isn’t physical. It exists only in the virtual world of the computer. That means it’s easy to make changes to any part of a program even after it is completely written. In contrast, if you wait until a bridge is fi nished and then tell your structural engineer that you’ve decided to add two extra lanes, there’s a good chance he’ll cackle wildly and offer you all sorts of creative but impractical suggestions for exactly what you can do with your two extra lanes. The fl exibility granted to software by its virtual nature is both a blessing and a curse. It’s a blessing because it lets you refi ne the program during development to better meet user needs, add new features to take advantage of opportunities discovered during implementation, and make modifi cations to meet evolving business needs. It even allows some applications to let users write scripts to perform new tasks never envisioned by developers. That type of fl exibility isn’t possible in other types of engineering. Unfortunately, the fl exibility that allows you to make changes throughout a software project’s life cycle also lets you mess things up at any point during development. Adding a new feature can break existing code or turn a simple, elegant design into a confusing mess. Constantly adding, removing, and modifying features during development can make it impossible for different parts of the system to work together. In some cases, it can even make it impossible to tell when the project is fi nished. Because software is so malleable, design decisions can be made at any point up to the end of the project. Actually, successful applications often continue to evolve long after the initial release. Microsoft Word, for example, has been evolving for roughly 30 years. (Sometimes for the better, sometimes for the worse. Remember Clippy? I’ll let you decide whether that change was for the better or for the worse, but I haven’t seen him in a while.) The fact that changes can come at any time means you need to consider the whole development process as a single, long, complex task. You can’t simply “engineer” a great design, turn the programmers loose on it, and walk off into the sunset wrapped in the warm glow of a job well done. The biggest design decisions may come early, and software development certainly has stages, but those stages are linked, so you need to consider them all together.

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• Software engineering

Snippet from Wikipedia: Software engineering

Software engineering is a branch of both computer science and engineering focused on designing, developing, testing, and maintaining software applications. It involves applying engineering principles and computer programming expertise to develop software systems that meet user needs.

The terms programmer and coder overlap software engineer, but they imply only the construction aspect of a typical software engineer workload.

A software engineer applies a software development process, which involves defining, implementing, testing, managing, and maintaining software systems, as well as developing the software development process itself.

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