AI Glossary & Dictionary for “M”
Find the Flux+Form AI glossary & dictionary to help you make sense of common AI terms. Below you can find an AI Glossary & Dictionary for “M”:
Machine Learning: A subset of AI that trains models on data to make predictions or decisions without being explicitly programmed.
Manifold Learning: Techniques that uncover low‑dimensional structures hidden in high‑dimensional data.
Margin: In classification, the distance between data points and the decision boundary; larger margins typically indicate better generalisation.
Marketing Automation: Software and AI‑driven workflows that streamline and automate marketing activities across channels.
Marketing Mix Modeling: Statistical analysis that quantifies the impact of different marketing channels on sales, enabling more efficient budget allocation.
Marketing Orchestration: Coordinating personalized marketing interactions across multiple channels using AI to determine the best next action.
Markov Decision Process: A mathematical framework for modelling decision making where outcomes are partly random and partly under the control of a decision maker.
Maximum Likelihood Estimation: A method for estimating model parameters by maximizing the probability of observing the given data.
Media Planning: The process of determining when, where and how often ads should run; AI models help optimise placements based on audience data.
Meta‑Learning: Techniques where models learn how to learn, enabling rapid adaptation to new tasks with minimal training examples.
Metric Learning: Learning a distance function that reflects task‑specific notions of similarity, improving recommendation and clustering.
Mixture Model: A probabilistic model that assumes data are generated from a mixture of underlying distributions, often used in segmentation.
Model Compression: Techniques for reducing the size of models to deploy them efficiently on edge devices.
Model Drift: Gradual degradation in model performance due to changing data distributions or customer behaviour.
Multi‑Armed Bandit: Algorithms that balance exploration and exploitation to maximise reward, used in real‑time ad selection.
Multi‑Modal Learning: Training models that can process and integrate multiple types of data (e.g., text, images and audio).
Multi‑Touch Attribution: Assigning credit to the multiple marketing interactions that influence a customer’s decision, often using AI models.
Multicollinearity: High correlation among independent variables in regression analysis, which can make estimates unstable.
Multiple Regression: Predicting an outcome variable using multiple predictors; common in marketing performance modelling.
This concludes the AI Glossary & Dictionary for “M”.