Glossary of Categories

Glossary of Categories

Term	Description
Active learning	A machine learning approach where the model selectively queries an oracle (e.g., a human annotator) for labels on the most informative samples, reducing the amount of labeled data needed for training.
Agriculture	The application of machine learning in agricultural practices, including crop prediction, pest detection, and yield optimization. Often involves satellite imaging and IoT data.
AI & society	The intersection of artificial intelligence with societal impacts, ethics, policy, and governance. Covers topics like AI regulation, workforce displacement, and responsible innovation.
AIF360 (AI Fairness 360)	An open-source toolkit from IBM for detecting and mitigating bias in machine learning models. Provides metrics for checking fairness and algorithms for bias mitigation across the ML pipeline.
Alphafold	DeepMind’s AI system for predicting protein 3D structures from amino acid sequences. A breakthrough in computational biology that has significantly advanced structural biology research.
Anomaly detection	Techniques for identifying unusual patterns, outliers, or rare events in data that do not conform to expected behavior. Applications include fraud detection, network security, and quality control. See also: OOD detection.
Athletics	The application of machine learning in sports analytics, including performance tracking, injury prediction, and game strategy optimization.
Audio	Resources related to audio data processing and analysis using machine learning techniques.
Audio data	Data in audio format (e.g., speech, music, environmental sounds) used as input for machine learning tasks such as classification, transcription, and generation.
Audio search	Using machine learning to search, identify, and retrieve audio content based on acoustic features, metadata, or content-based queries.
Autoencoder	A type of neural network that learns to compress data into a lower-dimensional representation and then reconstruct it. Used for dimensionality reduction, denoising, and generative modeling.
AWS (Amazon Web Services)	Amazon’s cloud computing platform offering a wide range of services for ML, including SageMaker for model training and deployment, and Bedrock for foundation model access.
Azure	Microsoft’s cloud computing platform providing ML services such as Azure Machine Learning, Cognitive Services, and OpenAI integrations for building and deploying AI solutions.
BadgerCompute	A UW-Madison computing resource providing access to computational infrastructure for research, including machine learning workloads.
Bedrock	Amazon Bedrock, an AWS service that provides access to foundation models from leading AI companies through a unified API for building generative AI applications.
Benchmarking	The practice of evaluating and comparing ML model performance using standardized datasets, metrics, and protocols. Essential for tracking progress and making informed model selections.
Bias	Systematic errors or unfairness in ML models that can arise from training data, algorithm design, or evaluation methods. Addressing bias is critical for building equitable AI systems.
Biology	The application of machine learning in biological sciences, including areas like genomics, ecology, protein modeling, and drug discovery.
Biophysics	The study of biological processes through the methods of physics. Machine learning is increasingly applied in areas like protein structure prediction and molecular dynamics simulations.
Blogs	Blog-format resources providing accessible explanations, tutorials, and commentary on machine learning topics.
Books	Comprehensive resources for learning and reference. Often written by experts, these provide in-depth coverage of machine learning topics.
Boosting	An ensemble machine learning technique that combines multiple weak learners sequentially, where each new model focuses on correcting errors made by the previous ones. Includes algorithms like AdaBoost, Gradient Boosting, and XGBoost.
Business	The application of machine learning in business contexts, including customer analytics, demand forecasting, and process automation.
Camera trap	The application of machine learning to classify and analyze images from remote wildlife cameras, aiding ecological research and conservation monitoring.
Carpentries	Hands-on, interactive learning sessions focusing on foundational coding and data skills. Facilitates skill acquisition through real-world examples and active learning.
CHTC (Center for High Throughput Computing)	A UW-Madison research center providing large-scale distributed computing resources for research, including ML workloads that require high-throughput processing.
CIFAR (Canadian Institute for Advanced Research)	Commonly refers to the CIFAR-10 and CIFAR-100 benchmark image classification datasets, widely used for evaluating computer vision models.
Citizen science	Public participation in scientific research, often aided by ML for tasks like data collection, labeling, and analysis at scale.
Classical ML (Classical Machine Learning)	Refers to traditional ML algorithms like SVMs, decision trees, and k-means clustering. These methods are well-established, interpretable, and often less resource-intensive than deep learning.
CLIP (Contrastive Language-Image Pre-training)	An OpenAI model that learns visual concepts from natural language supervision, connecting text and images in a shared embedding space. Enables zero-shot image classification and cross-modal retrieval.
Cloud	Cloud computing platforms and services (e.g., AWS, GCP, Azure) used for ML model training, deployment, and scaling. Provides on-demand access to GPUs and other resources.
Clustering	The process of grouping a set of objects in such a way that objects in the same group (cluster) are more similar to each other than to those in other groups. Common algorithms include k-means and DBSCAN.
CNN (Convolutional Neural Network)	A class of deep learning models particularly well-suited for image processing tasks. Known for their ability to automatically learn spatial hierarchies of features. Common libraries include TensorFlow and Keras.
CNN-LSTM (Convolutional Neural Network - Long Short-Term Memory)	A hybrid model combining CNNs for feature extraction from images or sequences and LSTMs for handling time dependencies. Used in applications like video classification.
Code-along	Live coding sessions where learners write code simultaneously with the instructor, which enhances learning through practice and immediate application of concepts.
Colab	Google Colaboratory, a free cloud-based Jupyter notebook environment that provides access to GPUs and TPUs. Widely used for ML experimentation and education.
ComBEE (Computational Biology, Ecology, and Evolution)	A UW-Madison community group focused on computational approaches in biology, ecology, and evolution, often leveraging machine learning methods.
Compute	Refers to computational resources, often in the context of machine learning, such as CPUs, GPUs, and cloud computing. Critical for training models, especially deep learning models.
Computer vision	A field of machine learning focused on enabling computers to interpret and make decisions based on visual data. Applications include image recognition, object detection, and facial recognition. Libraries include OpenCV, PyTorch, and TensorFlow.
Conformer	A convolution-augmented Transformer architecture that combines self-attention with convolutional layers, commonly used in speech and audio processing tasks.
Conservation	The application of machine learning in wildlife and environmental conservation, including species identification, habitat monitoring, and biodiversity assessment.
Contrastive learning	A self-supervised learning approach that trains models by contrasting positive pairs (similar samples) against negative pairs (dissimilar samples) to learn meaningful representations without labeled data.
Contribute	Involves contributing to open-source projects, fostering collaboration, improving software quality, and providing community support.
Cross Labs AI	Refers to interdisciplinary AI research and development across various labs or research groups, often involving collaboration between different fields such as computer science, biology, and engineering.
CSI (Cover Song Identification)	A task in music information retrieval that involves identifying whether one song is a cover version of another. Machine learning models for CSI often analyze harmonic, melodic, and rhythmic similarities across versions.
Data	General resources and concepts related to data collection, management, preprocessing, and curation for machine learning projects.
Decision trees	A tree-structured ML model that makes predictions by learning decision rules from data features. Interpretable and widely used for both classification and regression tasks. Forms the basis for ensemble methods like Random Forests and Gradient Boosting.
Deep learning	A subset of machine learning involving neural networks with many layers, used for complex tasks like image recognition and natural language processing. Commonly used libraries include TensorFlow, PyTorch, and Keras.
DeTox	A UW-Madison research initiative focused on detecting and mitigating toxicity in AI-generated content and online communications.
Diffusion	Diffusion models are a class of generative models that learn to create data by reversing a gradual noising process. Used for high-quality image, audio, and video generation. Examples include Stable Diffusion and DALL-E.
Drug synergy	The study and identification of drug combinations that produce a greater effect together than individually. Machine learning is used to predict synergistic drug pairs.
Early-fusion	A multimodal learning technique that combines data from different modalities (e.g., text and images) at the input level before processing, as opposed to late-fusion which combines after separate processing.
Ecology	The application of machine learning in ecological research, including species distribution modeling, ecosystem monitoring, and biodiversity analysis.
EDA (Exploratory Data Analysis)	The process of analyzing and visualizing datasets to summarize their main characteristics, discover patterns, and inform subsequent modeling decisions. A critical first step in any ML project.
Education	Resources related to teaching and learning about machine learning, including curricula, pedagogical approaches, and educational tools.
EHR (Electronic Health Records)	The application of machine learning to electronic health record data for tasks such as clinical prediction, patient risk stratification, and treatment recommendation.
Empirical patterns	Observed patterns in data identified through experimentation and analysis. These patterns help inform the development of models and algorithms.
Energy	The application of machine learning in the energy sector, including energy consumption forecasting, grid optimization, and renewable energy management.
Ethical AI	Practices and principles for developing AI systems that are fair, transparent, accountable, and aligned with human values. Encompasses bias mitigation, privacy, and responsible deployment.
Explainability	Methods and techniques for making ML model decisions understandable to humans, such as feature importance, attention visualization, and SHAP values. Related to interpretability and trustworthy AI.
Exploring AI@UW	A series exploring AI research, applications, and initiatives at the University of Wisconsin-Madison, showcasing interdisciplinary work across campus.
Fairness	Ensuring ML models treat different demographic groups equitably. Involves metrics for measuring disparate impact and algorithms for mitigating unfair outcomes.
First-steps	Introductory resources designed for beginners getting started with machine learning concepts, tools, and workflows.
Forest monitoring	Using machine learning for monitoring and analyzing forest ecosystems, including deforestation detection, tree species classification, and forest health assessment.
Forums	Online platforms where members of the machine learning community can ask questions, share knowledge, and collaborate on projects. Examples include Reddit, Stack Overflow, and specialized forums like Kaggle.
Foundation models	Large-scale pre-trained models that serve as a base for fine-tuning on specific tasks. They underpin many state-of-the-art NLP and computer vision systems. Examples include GPT-3, BERT, and CLIP.
GCP (Google Cloud Platform)	Google’s cloud computing platform offering ML services such as Vertex AI, TPU access, and AutoML for building and deploying machine learning models.
Gemini	Google’s family of multimodal AI models capable of processing and generating text, images, audio, and code. Successor to earlier models like PaLM.
GenAI (Generative AI)	AI models that can create new content including text, images, audio, and code. Encompasses technologies like large language models, diffusion models, and GANs.
Genomics	The study of genomes, often involving the analysis of DNA sequences. Machine learning aids in tasks like gene prediction, mutation analysis, and personalized medicine.
Geospatial data	Geographic and spatial data used in ML applications such as remote sensing, environmental monitoring, and location-based analytics.
Git/GitHub	Version control system (Git) and the associated platform (GitHub) for hosting and sharing code. Essential tools for collaboration and project management in software development, including ML projects.
GPU (Graphics Processing Unit)	A specialized processor widely used in deep learning for its parallel processing capabilities. Essential for training large neural networks efficiently.
GradCAM (Gradient-weighted Class Activation Mapping)	A visualization technique that produces heatmaps highlighting the important regions in an image for a CNN’s prediction, aiding model interpretability.
Grokking	A phenomenon in machine learning where a model unexpectedly generalizes well after many training iterations, often after initially performing poorly. Highlights the non-linear relationship between training time and model performance.
Guides	Detailed instructions or explanations, often in the form of tutorials or documentation, aimed at helping users understand and apply specific concepts or tools.
Healthcare	The application of machine learning in the healthcare industry, including areas like medical imaging, diagnostics, and personalized treatment plans. Common challenges include data privacy and interpretability.
Hugging Face	A popular platform for sharing pre-trained models, datasets, and other machine learning resources, especially in NLP. Provides tools like the Transformers library for easy model deployment.
Humanities	The application of machine learning in humanities research, including digital humanities, text analysis of historical documents, and computational approaches to cultural studies.
ICCV (International Conference on Computer Vision)	A top-tier academic conference in computer vision, featuring cutting-edge research on image recognition, 3D reconstruction, and visual understanding.
Image classification	The ML task of assigning a label or category to an entire image based on its visual content. A foundational task in computer vision with applications across many domains.
Image data	Data in image format (e.g., photographs, medical scans, satellite imagery) used as input for computer vision and other ML tasks.
Image preprocessing	Techniques for preparing image data before feeding it to ML models, including resizing, normalization, augmentation, and noise reduction.
Image processing	Computational techniques for manipulating and analyzing images, including filtering, enhancement, and transformation operations that often precede or complement ML pipelines.
Image segmentation	The ML task of partitioning an image into meaningful regions or segments, assigning a label to each pixel. Used in medical imaging, autonomous driving, and satellite image analysis.
Industry applications	Refers to the use of machine learning across various industries such as finance, manufacturing, and logistics, for tasks like predictive maintenance, fraud detection, and supply chain optimization.
Interpretability	The degree to which humans can understand the behavior, predictions, and decision-making process of an ML model. Critical for trust, debugging, and regulatory compliance.
IT Prof (IT Professional)	Resources and presentations targeted at IT professionals working with machine learning infrastructure, deployment, and operations.
Jupyter	An open-source interactive computing environment for creating notebooks that combine live code, equations, visualizations, and narrative text. Widely used in data science and ML workflows.
Keras	A high-level neural networks API written in Python, capable of running on top of TensorFlow, Theano, or CNTK. It allows for easy and fast prototyping of deep learning models.
Knowledge-based	Refers to models or systems that incorporate domain-specific knowledge, often encoded in rules or ontologies, to improve decision-making or interpretability. Examples include expert systems and knowledge graphs.
Label-efficient learning	Machine learning approaches designed to achieve good performance with fewer labeled examples, encompassing techniques like semi-supervised learning, active learning, and few-shot learning.
Libraries	Software libraries and toolkits that provide pre-built functions and tools for machine learning development, such as PyTorch, TensorFlow, Scikit-learn, and Hugging Face Transformers.
LLaVA (Large Language and Vision Assistant)	A multimodal AI model that combines language and vision understanding, capable of processing and generating both text and images.
LLM (Large Language Model)	A type of deep learning model that can process and generate human-like text by understanding context from vast amounts of data. Examples include GPT-3 and BERT.
LMM (Large Multimodal Model)	A class of models that can process and generate content across different modalities such as text, image, and audio. Examples include CLIP and DALL-E.
LoFTR (Local Feature Transformer)	A deep learning model for establishing pixel-level correspondences between images using transformers, effective for tasks like image stitching, 3D reconstruction, and visual localization.
LSTM (Long Short-Term Memory)	A type of recurrent neural network (RNN) designed to learn long-term dependencies in sequential data. Widely used in time-series prediction, speech recognition, and text generation.
Medical imaging	The application of machine learning techniques to analyze and interpret medical images. Common tasks include segmentation, classification, and anomaly detection. Libraries include MONAI, PyTorch, and TensorFlow.
Meteorology	The application of machine learning in weather and climate science, including weather forecasting, climate modeling, and extreme event prediction.
Microsoft Copilot	Microsoft’s AI assistant powered by large language models, integrated across Microsoft products for tasks like code completion, document drafting, and data analysis.
ML+X (Machine Learning + X)	The concept of combining machine learning with domain expertise across disciplines. The core focus of the ML+X Nexus community at UW-Madison.
ML4MI (Machine Learning for Medical Imaging)	A UW-Madison initiative focused on applying machine learning methods to medical imaging problems, fostering collaboration between computer scientists and medical researchers.
MLflow	An open-source platform for managing the end-to-end machine learning lifecycle, including experiment tracking, model packaging, and deployment.
MLOps (Machine Learning Operations)	Practices and tools for deploying, monitoring, and maintaining ML models in production environments. Bridges the gap between ML development and reliable system operation.
MLOPT	A UW-Madison research group focused on machine learning and optimization, exploring the intersection of optimization theory and practical ML applications.
Model exploration	Libraries that facilitate trying out different model architectures and pretrained models.
Model sharing	Practices and platforms for sharing trained ML models with the community, enabling reproducibility and reuse. Platforms include Hugging Face Hub and PyTorch Hub.
Models	Resources providing access to pre-trained or reference ML model implementations for various tasks and domains.
Multilingual	Machine learning approaches and models that work across multiple languages, including multilingual NLP, translation, and cross-lingual transfer learning.
Multimodal data	Data spanning multiple modalities (e.g., text, image, audio, video) used together to provide richer information for ML models.
Multimodal learning	Machine learning approaches that process and integrate information from multiple data modalities (e.g., text and images, audio and video) to improve understanding and predictions.
Music	The application of machine learning in music analysis, generation, classification, and information retrieval.
Music transcription	Using machine learning to automatically convert audio music recordings into symbolic notation (e.g., sheet music or MIDI), including pitch detection and rhythm analysis.
NLP (Natural Language Processing)	A field of machine learning focused on the interaction between computers and humans using natural language. Common libraries include NLTK, SpaCy, and Hugging Face Transformers.
Notebooks	Jupyter or Quarto notebook-format resources that combine executable code, visualizations, and narrative text for interactive learning and reproducible analysis.
Novelty detection	Techniques for identifying data points that are significantly different from the training distribution, related to but distinct from anomaly detection and OOD detection.
Object detection	The ML task of identifying and localizing objects within images by drawing bounding boxes and assigning class labels. Common models include YOLO, Faster R-CNN, and DETR.
OCR (Optical Character Recognition)	The use of machine learning to convert images of text (handwritten, printed, or scanned) into machine-readable text. Applications include document digitization and text extraction.
OOD detection (Out-of-Distribution Detection)	Techniques for identifying data points that do not belong to the distribution on which a model was trained. Important for building robust and trustworthy models. Common methods include Mahalanobis distance and energy-based models.
Open-set recognition	A classification paradigm where the model must not only classify known categories but also identify and reject samples from unknown classes not seen during training. See also: OOD detection.
Perception	ML systems that interpret sensory data such as vision, audio, and touch to understand and interact with the physical world. Central to robotics and autonomous systems.
Physics	The application of machine learning in physics research, including particle physics, condensed matter, and physics-informed neural networks.
Plant phenotyping	Using machine learning to measure, analyze, and predict plant traits from imaging data, supporting agricultural research and crop improvement.
Prompt engineering	Techniques for designing and optimizing input prompts to elicit desired behaviors from large language models and other generative AI systems.
Protein engineering	Using machine learning to design, modify, and optimize protein sequences and structures for desired properties, accelerating drug design and biotechnology.
Protein language models	Language models trained on protein amino acid sequences to learn evolutionary and structural patterns, used for predicting protein function, structure, and fitness.
Python	A high-level programming language that has become the de facto standard for machine learning and data science due to its readability and vast ecosystem of libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow).
PyTorch	An open-source machine learning library based on the Torch library, primarily used for applications such as computer vision and natural language processing. Developed by Facebook’s AI Research lab.
PyTorch-OOD	A PyTorch library providing implementations of out-of-distribution detection methods, making it easy to benchmark and apply OOD detection techniques.
RAG (Retrieval-Augmented Generation)	A technique that combines information retrieval systems with large language models, allowing the model to access and reference external knowledge to generate more accurate and grounded responses.
Regression	A supervised ML technique for predicting continuous numerical values based on input features. Common methods include linear regression, polynomial regression, and neural network-based regression.
Remote sensing	The application of machine learning to analyze satellite, aerial, or drone imagery for tasks like land use classification, environmental monitoring, and disaster response.
Representation learning	Machine learning methods that automatically discover and learn useful representations or features from raw data, reducing the need for manual feature engineering.
Reproducibility	Ensuring that ML experiments and results can be consistently replicated by others, a key principle in scientific research. Often involves detailed documentation, version control, and the use of containers.
Retrieval	Information retrieval techniques that search and rank relevant documents or data, often used in conjunction with LLMs for retrieval-augmented generation and semantic search.
RT-DETR (Real-Time Detection Transformer)	An object detection model based on the DETR architecture, optimized for real-time processing. Suitable for applications requiring fast detection like video analysis and autonomous navigation.
SageMaker	Amazon SageMaker, a fully managed AWS service for building, training, and deploying machine learning models at scale with built-in tools for every step of the ML workflow.
SAM (Segment Anything Model)	Meta’s foundation model for image segmentation that can segment any object in an image using prompts like points, boxes, or text, enabling zero-shot generalization across segmentation tasks.
Science communication	The practice of communicating scientific and ML research findings to broader audiences, including visualization, writing, and presentation techniques.
Scientific Text Mining	Using machine learning to extract structured information, relationships, and knowledge from scientific literature and research publications.
Semi-supervised	A machine learning paradigm that uses both a small amount of labeled data and a larger amount of unlabeled data for training, bridging supervised and unsupervised approaches.
Signal processing	Techniques for analyzing, modifying, and synthesizing signals (e.g., audio, sensor data), often combined with ML methods for tasks like noise reduction, feature extraction, and pattern recognition.
SILO	A UW-Madison initiative (Science and Integrated Learning Opportunities) that brings together researchers across disciplines, including those applying machine learning in various scientific domains.
Simulations	Using computational simulations to generate synthetic data or validate ML models, common in fields like physics, biology, and engineering where real data may be scarce.
Sklearn (Scikit-learn)	A machine learning library for Python that provides simple and efficient tools for data mining and data analysis, built on NumPy, SciPy, and Matplotlib.
Spectral analysis	The analysis of frequency-domain representations of signals or data, used in audio processing, remote sensing, and other domains alongside ML methods.
Statistical learning	Machine learning methods grounded in statistical theory, emphasizing inference, uncertainty quantification, and the mathematical foundations of learning from data.
Summarization	Using machine learning, particularly NLP models, to automatically generate concise summaries of longer texts while preserving key information.
Sustainability	The application of machine learning to environmental sustainability challenges, including climate modeling, resource optimization, and green computing practices.
SVM (Support Vector Machine)	A classical ML algorithm that finds the optimal hyperplane for separating classes in feature space. Effective for both classification and regression, especially in high-dimensional spaces.
Tabular	Machine learning methods and resources focused on structured tabular data (rows and columns), including techniques like gradient boosting, random forests, and neural approaches for tabular prediction.
Text analysis	The process of deriving information from text data, often involving techniques from NLP. Used in sentiment analysis, topic modeling, and more. Common libraries include NLTK, SpaCy, and Gensim.
Text data	Data in text format used for NLP and other ML tasks, including documents, web pages, social media posts, and scientific articles. See also: NLP.
Text extraction	Techniques for extracting text content from documents, images, or other media, combining OCR, layout analysis, and NLP methods.
Text mining	The process of discovering patterns, trends, and useful information from large collections of text data using ML and NLP techniques.
Time-series	Machine learning methods for analyzing and predicting sequential, time-dependent data. Applications include forecasting, anomaly detection, and signal processing.
Topic modeling	An unsupervised NLP technique for discovering abstract topics in text collections. Common algorithms include Latent Dirichlet Allocation (LDA) and neural topic models.
Transfer learning	A machine learning technique where knowledge gained from training on one task is applied to improve performance on a different but related task, reducing the need for large labeled datasets.
Transformer	A neural network architecture based on self-attention mechanisms, foundational to modern NLP and increasingly used in computer vision. Originally introduced in the “Attention Is All You Need” paper.
Trustworthy AI	Approaches and practices that ensure AI systems are reliable, fair, transparent, and robust, especially in critical applications like healthcare or finance. Encompasses fairness, explainability, and safety.
Udacity	An online learning platform offering courses, including nanodegree programs, on a variety of topics including machine learning and data science. Often includes projects and code-alongs.
Ultrasound	The application of machine learning to ultrasound imaging data for tasks such as automated diagnosis, image segmentation, and quality enhancement in medical settings.
U-Net	A convolutional neural network architecture originally designed for biomedical image segmentation, featuring a symmetric encoder-decoder structure with skip connections for precise localization.
Unsupervised learning	Machine learning approaches that learn patterns and structure from unlabeled data, including clustering, dimensionality reduction, and generative modeling.
UW-Madison (University of Wisconsin-Madison)	Resources, research, and initiatives from the University of Wisconsin-Madison related to machine learning and AI.
Videos	Video-format resources including recorded lectures, tutorials, conference talks, and workshop recordings for learning machine learning topics.
Visualization	Techniques for visually representing data, model architectures, training progress, and ML model outputs to aid understanding and communication.
ViT (Vision Transformer)	Based on the transformer architecture, adapted for vision tasks. Images are split into patches (usually 16x16), which are flattened and treated as input tokens, then processed similarly to how words are processed in NLP transformers like BERT.
VLM (Visual-Language Model)	A type of machine learning model that understands and generates content based on both visual and textual inputs, often used in tasks like image captioning and visual question answering. Examples include CLIP and LLaVA.
Webex	Cisco Webex, a video conferencing platform. Used here to tag recorded presentations and meetings available as learning resources.
Wildlife	The application of machine learning in wildlife research and conservation, including species identification from camera traps, population monitoring, and habitat analysis.
Workshops	Workshop-format educational resources providing structured, hands-on learning experiences on specific machine learning topics and tools.
XGBoost (Extreme Gradient Boosting)	An optimized gradient boosting library for classification and regression that is highly efficient, flexible, and widely used in ML competitions and industry applications.
YOLO (You Only Look Once)	A family of real-time object detection models that predict bounding boxes and class labels in a single forward pass, known for their speed and efficiency.
Zero-shot learning	A machine learning paradigm where models can classify or generate outputs for classes or tasks not seen during training, often leveraging semantic knowledge or natural language descriptions.
Zoom	A video conferencing platform. Used here to tag recorded presentations and meetings available as learning resources.