Core AI Concepts Flashcards
Microsoft Azure AI Fundamentals AI-900 Flashcards
| Front | Back |
| Define Reinforcement Learning | A type of machine learning where an agent learns to make decisions by receiving rewards or penalties |
| Define Supervised Learning | A type of machine learning where the model is trained on labeled data |
| Define Unsupervised Learning | A type of machine learning where the model identifies patterns and relationships in unlabeled data |
| What are the main types of Machine Learning | Supervised Learning, Unsupervised Learning and Reinforcement Learning |
| What is Accountability in AI | Holding developers and organizations responsible for the outcomes of AI systems |
| What is Anomaly Detection | A method used to identify unusual patterns or outliers in data |
| What is Artificial Intelligence | The simulation of human intelligence in machines that perform tasks typically requiring human intelligence |
| What is Classification | A supervised learning task that assigns a category label to data |
| What is Data Science | An interdisciplinary field focused on extracting insights from data using various techniques |
| What is Deep Learning | A specialized branch of machine learning using neural networks with many layers to analyze complex patterns |
| What is Fairness in AI | Ensuring AI systems provide unbiased results and do not discriminate against certain groups |
| What is Machine Learning | A subset of AI that uses algorithms to allow computers to learn from data and make predictions or decisions |
| What is Regression | A supervised learning task that predicts a continuous value based on input data |
| What is the difference between AI Machine Learning and Deep Learning | AI is the overall concept ML is a subset of AI and Deep Learning is a specialized branch of ML |
| What is Transparency in AI | The ability to understand and interpret how an AI system makes decisions |
About the Flashcards
Flashcards for the Microsoft Azure AI Fundamentals exam provide concise, exam-focused review of foundational terminology and concepts in artificial intelligence and its subfields. Cards define Artificial Intelligence, Machine Learning, and Deep Learning, explain the role of algorithms, and compare AI, ML, and Deep Learning while reinforcing core distinctions and high-level definitions students must know.
The deck also covers main machine learning types - supervised, unsupervised, and reinforcement learning - and common tasks such as classification, regression, and anomaly detection. Ethics topics like fairness, transparency, and accountability are included, along with an overview of data science concepts to help students connect methods to real-world data problems.
Topics covered in this flashcard deck:
- Artificial Intelligence concepts
- Machine Learning types
- Deep Learning basics
- Classification and regression
- Anomaly detection
- Fairness, transparency, accountability