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• Abductive logic programming –
• Abductive reasoning –
• Abstract data type –
• 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 –
• 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 –
• 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 –
• 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 –
• Cognitive architecture –
• Cognitive computing –
• Cognitive science –
• Combinatorial optimization –
• Committee machine –
• 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 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 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 –

• 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 –
• Deep learning –
• Default logic –
• Description logic –
• Developmental robotics –
• 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 –
• Ethics of artificial intelligence –
• Evolutionary algorithm –
• Evolutionary computation –
• Evolving classification function –
• Existential risk –
• Expert systems –

• Fast-and-frugal trees –
• Feature extraction –
• Feature learning –
• Feature selection –
• First-order logic –
• Fluent –
• Formal language –
• Forward chaining –
• 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 –
• Graph –
• Graph database –
• Graph theory –
• Graph traversal –

• 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 –
• 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 –
• 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 –
• Nondeterministic algorithm –
• Nouvelle AI –
• NP –
• NP-completeness –
• NP-hardness –

• Occam's razor –
• Offline 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 –
• Predicate logic –
• Predictive analytics –
• Principal component analysis –
• Principle of rationality –
• Probabilistic programming language –
• Production Rule Representation –
• Production system –
• Programming language –
• Prolog –
• Propositional calculus –
• Python –

• Qualification problem –
• Quantifier –
• Quantum computing –
• Query 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 –
• Self-management –
• Semantic network –
• Semantic reasoner –
• Semantic query –
• Semantics –
• Sensor fusion –
• Separation logic –
• Similarity learning –
• Simulated annealing –
• Situated approach –
• Situation calculus –
• SLD resolution –
• Soft computing –
• Software –
• Software engineering –
• Spatial-temporal reasoning –
• SPARQL –
• Speech recognition –
• Spiking neural network –
• 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 –
• Weak AI –
• World Wide Web Consortium –

• Artificial intelligence
• Glossary of machine vision

Return to Programming glossary, Computer science glossary

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

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

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

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 design, development, use, and 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 an engineering approach to software development. A practitioner, a software engineer, applies the engineering design process to develop software.

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

A software engineer applies a software development process, which involves the definition, implementation, testing, management and maintenance of software systems and with development of the software development process itself.

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