Activation Function: In neural networks, this function decides whether a neuron should be activated or not, based on the weighted sum of its input.

Adversarial Examples: Input data that is intentionally designed to fool the AI system, leading it to make incorrect decisions.

Algorithmic Bias: Systematic errors in the output of an algorithm, often resulting from biased data used in the learning process.

Artificial General Intelligence (AGI): A type of AI that can understand, learn, and apply intelligence to any intellectual task that a human being can.

Artificial Intelligence (AI): The branch of computer science focused on creating machines and systems capable of learning and applying knowledge similar to humans.

Artificial Neural Networks (ANNs): Computing models inspired by the human brain, used in ML and DL for learning from data.

Association Rule Learning: A rule-based ML method for discovering relationships between variables in large datasets.

Attention Mechanism: In neural networks, it allows the model to focus on certain parts of the input data, similar to how humans pay attention to certain details.

Augmented Intelligence: AI systems that work collaboratively with human intelligence, rather than replacing it.

Augmented Reality: A technology that superimposes a computer-generated image on a user's view of the real world.

Automated Machine Learning (AutoML): The process of automating the end-to-end process of applying machine learning to real-world problems.

Automatic Speech Recognition (ASR): Technology that converts spoken language into written text.

Backpropagation: A method used in neural networks to adjust the weights of neurons by calculating the gradient of the loss function.

Backpropagation Through Time (BPTT): A method for training certain types of neural networks such as recurrent neural networks (RNNs).

Bayesian Networks: A type of probabilistic graphical model that uses Bayesian inference for probability computations.

Batch Normalization: A technique to improve the performance and stability of artificial neural networks.

Bias-Variance Tradeoff: A dilemma when training ML models where improving model's bias may increase its variance, and vice versa.

Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.

Big Data Analytics: The process of collecting, organizing and analyzing large sets of data to discover patterns and other useful information.

Chatbot: A computer program that uses NLP to interact with humans in natural language.

Classification Models: These are models used to separate data into different categories.

Cloud Computing: The delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet.

Cognitive Computing: Systems that learn at scale, reason with purpose, and interact with humans naturally.

Collaborative Filtering Recommendation Systems: Systems that predict a user's interests by collecting preferences from many users.

Collaborative Robotics: The study and design of robots that work side by side with humans.

Computer Vision: An interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.

Convolutional Neural Networks (CNNs): A class of deep learning neural networks, most commonly applied to analyzing visual imagery.

Cross-Validation: A resampling procedure used to evaluate machine learning models on a limited data sample.

Data Augmentation: A strategy that enables practitioners to significantly increase the diversity

Data Mining: The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Data Science: A multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Decision Trees: A decision support tool that uses a tree-like model of decisions and their possible consequences.

Deep Belief Networks (DBNs): A generative graphical model, composed of multiple layers of latent variables with connections between the layers but not between units within each layer.

Deep Convolutional Neural Networks (DCNNs): A class of deep learning models, primarily used in the field of computer vision.

Deep Learning: Part of a broader family of machine learning methods based on artificial neural networks with representation learning.

Deep Q-Network (DQN): A reinforcement learning algorithm that combines Q-Learning with a deep neural network.

Deep Reinforcement Learning: A subfield of AI where reinforcement learning and deep learning are combined to create algorithms capable of learning from raw input data.

Differential Evolution (DE): A method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

Dimensionality Reduction: The process of reducing the number of random variables under consideration by obtaining a set of principal variables.

Explainable Artificial Intelligence (XAI): AI systems that provide a clear, understandable explanation of their actions and decision-making processes to the average user.

Fault Diagnosis: The process of identifying, isolating, and defining faults in industrial machines.

Fuzzy Logic: A computational paradigm capable of handling the concept of partial truth, where something can be true and false to different degrees.

Generative Adversarial Networks (GANs): A class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014.

Generative Models: In statistics, generative models are used to generate new sample/data instances.

Genetic Algorithms: Search-based algorithms inspired by the process of natural selection that belong to the larger class of evolutionary algorithms.

Human-in-the-Loop (HITL): An approach involving human interaction during the training phase of machine learning and AI systems.

Image Interpretation: The process of examining an image to identify objects and judge their significance.

Information Retrieval: The process of obtaining information system resources that are relevant to an information need from a collection of those resources.

Intelligent Transportation Systems (ITS): Systems that aim to provide innovative services relating to different modes of transport and traffic management.

Interpretability: The degree to which a human can understand the cause of a decision made by a machine learning model.

Knowledge Discovery: The process of discovering useful knowledge from a collection of data.

Language Modeling: The task of assigning probabilities to sequences of words. This means that, given a certain number of words, it assigns a probability to the next word in the sequence.

Machine Learning: A type of artificial intelligence that enables computers to learn and make decisions without being explicitly programmed.

Machine Translation: The application of computers to the task of translating texts from one language to another.

Medical Diagnostic Systems: AI systems that are designed to provide advice in medical decision-making.

Modeling Techniques: A variety of methods used to create a simplified representation of the workings of a complex real-world system.

Natural Language Processing Systems (NLP): The intersection of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human languages.

Neural Networks: Computing systems with interconnected nodes that work much like neurons in the human brain. They are designed to recognize patterns and interpret sensory data.

Neuroevolution: A form of machine learning that uses evolutionary algorithms to train artificial neural networks.

Online Learning: A machine learning method where the model learns as data becomes available.

Ontology Learning: The automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts that these terms represent from a corpus of natural language text, and encoding them with an ontology language for easy retrieval.

OpenAI: An artificial intelligence research lab made up of both for-profit and non-profit arms.

Particle Swarm Optimization (PSO): A computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality, inspired by the social behavior of bird flocking or fish schooling.

Planning and Scheduling: The practice of making decisions about tasks, resources, and timing to achieve goals or meet deadlines.

Preprocessing: The initial steps to clean and transform raw data before it is processed and analyzed.

Probabilistic Models: Models of systems that include some randomness. They use statistics and probability theory to predict different outcomes.

Process Mining: A family of techniques in the field of process management that support the analysis of business processes based on event logs.

Question Answering Systems: AI systems designed to answer questions posed by humans in a natural language.

Random Forests: A learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees.

Real-time Data Processing: The process of computing data as soon as it becomes available.

Recommender Systems: Algorithms aimed at suggesting relevant items to users (items being movies to watch, text to read, products to buy, or anything else depending on industries).

Reinforcement Learning: An area of machine learning where an agent learns to behave in an environment, by performing actions and seeing the results.

Robotics Process Automation (RPA): The use of software with artificial intelligence (AI) and machine learning capabilities to handle high-volume, repeatable tasks.

Robotics Systems: The set of elements and rules that make up the structure and operation of robots.

Rule-based Systems: A system that uses rules, in the form of "if-then" statements, to drive the behavior of the system.

Sentiment Analysis Systems: The use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

Speech Processing: The study of speech signals and the processing methods of signals. The signals are usually processed in a digital representation, so speech processing can be regarded as a special case of digital signal processing.

Supervised Learning: A type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions.

Support Vector Machines (SVM): A set of supervised learning methods used for classification, regression and outliers detection.

Swarm Intelligence: A type of artificial intelligence based on the collective behavior of decentralized

Text Analysis: The process of converting unstructured text data into meaningful insights or structured data.

Transfer Learning: The improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned.

Unsupervised Learning: A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.

Variance Reduction: A step in the machine learning process intended to reduce the complexity of a model by reducing overfitting and noise, and improving the accuracy of predictions.

Virtual Assistant: An application program that understands natural language voice commands and completes tasks for the user.

Voice User Interface (VUI): A user interface that allows for interaction by means of voice or speech.

Wearable Technology: Devices that are worn on the body, either as an accessory or as part of the material used in clothing.

Web Mining: The application of data mining techniques to discover patterns from the web.

X-ray Inspection Systems: Systems that use x-rays to inspect the inside of objects. Often used in security and manufacturing settings.

Yield Optimization: The process of continually improving the yield of a product or process by reducing defects and variability.

Zero-shot Learning: A problem setup in machine learning where the learning system needs to accurately classify instances it has not seen during training.