Artificial Intelligence (AI) – The branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

Autoencoders – Neural network architectures used for unsupervised learning and dimensionality reduction. Autoencoders aim to learn a compressed representation of input data by training an encoder and a decoder network to reconstruct the original input, enabling efficient data compression and feature extraction.

Backpropagation – A key algorithm used to train neural networks by computing the gradients of the loss function with respect to the model’s parameters. Backpropagation propagates the error back through the network, allowing for efficient parameter updates and learning.

Bayesian Inference – A probabilistic framework used to make predictions and update beliefs based on prior knowledge and observed data. Bayesian inference involves calculating posterior probabilities by combining prior probabilities and likelihood functions, allowing for principled uncertainty estimation and decision-making.

Chatbots – AI-based virtual agents designed to interact and communicate with humans through natural language. Chatbots utilize NLP techniques and dialogue systems to understand user queries and provide relevant responses or perform tasks.

Clustering – A technique in unsupervised learning used to group similar data points together based on their inherent characteristics or patterns. Clustering algorithms aim to discover the underlying structure or relationships within data without prior knowledge of the class labels.

Computer Vision – The field of AI that focuses on enabling computers to understand and interpret visual information from images or videos. It involves tasks like object recognition, image classification, and image segmentation.

Convolutional Neural Networks (CNNs) – A type of neural network designed for processing grid-like data, such as images or sequences. CNNs use convolutional layers to automatically learn spatial hierarchies of patterns or features from the input data.

Cross-Validation – A technique used to assess the performance and generalization ability of a machine learning model. Cross-validation involves splitting the data into multiple subsets, training the model on a portion of the data, and evaluating its performance on the remaining data. It helps to estimate how well the model will perform on unseen data.

Decision Trees – A machine learning algorithm that uses a hierarchical structure of decision nodes and branches to model decisions or classifications. Decision trees are easy to interpret and can handle both numerical and categorical data.

Deep Learning – A subfield of machine learning that utilizes artificial neural networks with multiple layers to learn and extract high-level representations from complex and large-scale data, enabling the development of highly accurate models for tasks such as image and speech recognition.

Dimensionality Reduction – The process of reducing the number of input variables or features in a dataset while preserving important information. Dimensionality reduction techniques, such as Principal Component Analysis (PCA) and t-SNE, help to overcome the curse of dimensionality, improve computational efficiency, and visualize high-dimensional data.

Ensemble Learning – A machine learning technique that combines predictions from multiple models (ensemble members) to make more accurate and robust predictions. Ensemble methods, such as bagging and boosting, reduce the risk of overfitting and improve generalization by leveraging the diversity and collective knowledge of the individual models.

Ethics in AI – The study and application of moral principles and guidelines to ensure the responsible development and deployment of AI systems. Ethical considerations in AI involve addressing issues such as fairness, transparency, privacy, bias mitigation, and societal impact, with the goal of promoting the ethical use of AI technology.

Expert Systems – AI systems designed to replicate the knowledge and decision-making processes of human experts in a specific domain. Expert systems use rules, heuristics, and knowledge representation to provide advice, solve problems, or make recommendations.

Explainable AI (XAI) – The field of research that aims to develop AI systems and algorithms that can provide transparent and understandable explanations for their decisions and actions. XAI techniques help to increase trust, accountability, and interpretability of AI systems, enabling users to understand the reasoning behind AI-generated outcomes.

Generative Adversarial Networks (GANs) – A class of neural network architectures that consists of a generator network and a discriminator network that compete against each other. GANs are used to generate realistic synthetic data, such as images or text.

Gradient Descent – An iterative optimization algorithm used to train machine learning models by minimizing a loss function. Gradient descent updates the model’s parameters in the direction of steepest descent of the loss function gradient, gradually converging towards the optimal set of parameters.

Hyperparameters – Parameters that are not learned by the machine learning algorithm itself but are set by the user before training. Hyperparameters control the behavior and performance of the model, such as learning rate, regularization strength, or the number of hidden layers in a neural network. Hyperparameter tuning is the process of finding the optimal values for these parameters.

Image Recognition – The process of identifying and classifying objects or patterns within digital images. Image recognition techniques utilize computer vision and machine learning algorithms to analyze visual content and extract meaningful information.

Knowledge Representation – The process of structuring and organizing information in a way that can be effectively used by AI systems. Knowledge representation techniques aim to capture and model human knowledge, enabling machines to reason, infer, and make intelligent decisions.

Logic Programming – A programming paradigm that uses mathematical logic to represent and solve problems. Logic programming languages, such as Prolog, facilitate the development of rule-based systems and reasoning engines.

Machine Learning (ML) – A subset of AI that focuses on the development of algorithms and models that allow computers to learn and make predictions or decisions based on patterns and data, without being explicitly programmed.

Markov Decision Processes (MDPs) – A mathematical framework used for modeling decision-making problems involving sequential actions and uncertain outcomes. MDPs are employed in reinforcement learning to formulate and solve optimization problems in dynamic environments.

Natural Language Processing (NLP) – The area of AI concerned with enabling computers to understand, interpret, and generate human language. It involves tasks such as language translation, sentiment analysis, and speech recognition.

Natural Language Generation (NLG) – The process of generating human-like text or language by computers. NLG systems analyze data and generate coherent and contextually appropriate text, enabling applications such as automated report generation or chatbot responses.

Neural Networks – Computational models inspired by the structure and function of the human brain, consisting of interconnected nodes (neurons) organized in layers. They process information and learn by adjusting the strengths of connections (weights) between neurons.

Overfitting and Underfitting – Phenomena that occur during machine learning when a model performs poorly due to either excessive complexity or lack of flexibility. Overfitting happens when a model fits the training data too closely and fails to generalize well to unseen data. Underfitting occurs when a model is too simple to capture the underlying patterns and fails to learn from the training data effectively.

Probabilistic Graphical Models – Statistical models that represent the probabilistic relationships between random variables using directed or undirected graphs. Probabilistic graphical models, such as Bayesian networks and Markov networks, are used for reasoning under uncertainty and performing probabilistic inference.

Recurrent Neural Networks (RNNs) – Neural networks that can process sequential data by utilizing feedback connections. RNNs maintain internal memory, allowing them to capture dependencies and context over time, making them suitable for tasks like speech recognition and language modeling.

Regression – A type of supervised learning that models the relationship between input variables and continuous output variables. Regression algorithms are used to predict numerical values or estimate trends based on training data.

Reinforcement Learning – A type of machine learning where an agent learns to make decisions by interacting with an environment. It learns through trial and error, receiving feedback in the form of rewards or penalties, with the goal of maximizing cumulative rewards.

Regularization – Techniques used to prevent overfitting in machine learning models by adding additional constraints or penalties to the loss function. Regularization methods, such as L1 and L2 regularization, encourage the model to be simpler and reduce the influence of irrelevant features, improving generalization and reducing the risk of overfitting.

Sentiment Analysis – A technique that determines the sentiment or subjective opinion expressed in text, typically for tasks like sentiment classification, opinion mining, or social media analysis. Sentiment analysis helps to extract insights from large volumes of text data.

Speech Recognition – The technology that enables computers to convert spoken language into written text. Speech recognition systems use techniques such as acoustic modeling and language modeling to accurately transcribe spoken words or commands.

Stochastic Gradient Descent (SGD) – A variant of gradient descent that updates the model’s parameters using a random subset of the training data at each iteration. SGD is computationally efficient and is commonly used in large-scale machine learning tasks.

Support Vector Machines (SVM) – A supervised learning algorithm used for classification and regression tasks. SVMs find optimal hyperplanes in a high-dimensional space to separate or classify data points based on their features.

Transfer Learning – The practice of leveraging knowledge gained from one task or domain to improve learning or performance in another related task or domain. It enables models to leverage pre-trained features and knowledge for faster and more effective learning.

Unsupervised Learning – A machine learning approach where models learn from unlabeled data, seeking patterns or structures within the data without explicit labels or guidance. It is used for tasks like clustering, dimensionality reduction, and anomaly detection.

XAI (Explainable AI) – The field of research that aims to develop AI systems and algorithms that can provide transparent and understandable explanations for their decisions and actions. XAI techniques help to increase trust, accountability, and interpretability of AI systems, enabling users to understand the reasoning behind AI-generated outcomes.</p>