Class 10 Artificial Intelligence Curriculum Flashcards

Comprehensive vocabulary flashcards covering the CBSE Grade X AI Curriculum across all 6 units.

AI Project Cycle

The cyclical process followed to complete an AI project, consisting of six stages: Problem Scoping, Data Acquisition, Data Exploration, Modelling, Evaluation, and Deployment.

Problem Scoping

The first stage of the AI Project Cycle where the goal for the AI project is set by stating the problem to be solved and looking at parameters affecting it.

Data Acquisition

The stage of the AI Project Cycle that involves collecting data from reliable and authentic sources to form the base of the AI project.

Data Exploration

The stage of the AI Project Cycle where acquired data is visually represented using graphs, databases, flow charts, or maps to interpret underlying patterns.

Statistical Data (AI Domain)

A domain of AI related to data systems and processes in which the system collects data, maintains datasets, and derives meaning to make decisions.

Computer Vision (CV)

A domain of AI depicting the capability of a machine to get, analyze, and process visual information (such as photographs or videos) and predict decisions based on it.

Natural Language Processing (NLP)

A branch of AI dealing with the interaction between computers and humans using natural language, enabling computers to read, decipher, and understand spoken and written words.

Ethical Frameworks

Frameworks that provide step-by-step guidance on problem-solving to ensure that choices made do not cause unintended harm.

Bioethics

A sector-based ethical framework used in healthcare and life sciences that addresses moral issues related to health, medicine, and biological sciences.

Non-maleficence

The bioethical principle of avoiding causing harm or negative consequences and prioritizing actions that minimize harm to individuals, communities, or the environment.

Beneficence

The bioethical principle of promoting and maximizing the well-being and welfare of individuals and society by taking actions that ensure maximum benefit.

Artificial Intelligence (AI)

The umbrella terminology referring to any technique that enables computers to mimic human intelligence using algorithms and data.

Machine Learning (ML)

A subset of AI that enables machines to learn from data and improve at tasks with experience without being explicitly programmed.

Deep Learning (DL)

A subset of ML that uses algorithms inspired by the human brain (artificial neural networks) to enable software to train itself using vast amounts of data.

Features

The attributes or characteristics describing data points, represented as columns in a tabular dataset.

Labels

Special features in a dataset that represent the target output or category being predicted.

Rule-Based Approach

An AI modeling approach where relationships or patterns in data are explicitly defined by developer-written rules, resulting in static learning.

Learning-Based Approach

An AI modeling method where the machine analyzes data independently to learn patterns, rules, or algorithms adaptive to changing data.

Supervised Learning

A machine learning approach where the model is trained using labeled data containing both features and corresponding target output labels.

Unsupervised Learning

A machine learning approach where the model works on unlabeled datasets to independently discover patterns, relationships, or clusters.

Reinforcement Learning

A machine learning approach where an agent learns to perform tasks through trial-and-error by maximizing a reward metric based on feedback from its environment.

Classification Model

A supervised learning model used when the target variable is categorical or discrete, assigning data into predefined classes.

Regression Model

A supervised learning model used to predict continuous numeric values based on input variables.

Clustering

An unsupervised learning method that groups similar unlabeled data points into clusters based on shared characteristics.

Association Rule

An unsupervised learning method used to discover interesting relationships or co-occurrence patterns between variables in large databases.

Artificial Neural Network (ANN)

A deep learning model loosely structured like human brain neurons that automatically extracts features from large datasets through interconnected nodes across layers.

Model Evaluation

The process of using specific evaluation metrics to assess a machine learning model's performance and guide constructive improvements.

Train-Test Split

A technique used to evaluate machine learning algorithms by dividing a dataset into a training subset to learn patterns and a testing subset to assess performance on unseen data.

Accuracy

An evaluation metric measuring the ratio of total correct predictions made by a model to the total number of predictions made, defined as $\text{Accuracy} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}}$.

Confusion Matrix

A tabular grid layout comparing actual target values against model-predicted values across positive and negative classes.

True Positive (TP)

The outcome where the model correctly predicts the positive class when the actual value is positive.

True Negative (TN)

The outcome where the model correctly predicts the negative class when the actual value is negative.

False Positive (FP)

The outcome where the model incorrectly predicts the positive class when the actual value is negative.

False Negative (FN)

The outcome where the model incorrectly predicts the negative class when the actual value is positive.

Precision

The ratio of correctly predicted positive examples to the total predicted positive examples, calculated as $\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}$.

Recall

The ratio of correctly predicted positive examples to the total actual positive instances, calculated as $\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}$.

F1 Score

The harmonic mean of precision and recall, used to evaluate models on unbalanced datasets, calculated as $\text{F1 Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}$.

Data Science

A concept unifying statistics, data analysis, machine learning, and related methods to analyze and extract insights from data.

No-Code AI

A paradigm that allows non-technical users to build, train, and deploy AI/ML models through visual drag-and-drop interfaces without writing programming code.

Automation Bias

The human tendency to favor suggestions from automated decision-making systems and ignore contradictory non-automated information.

Mean

A descriptive statistic representing the central average value of a numeric dataset.

Median

A descriptive statistic representing the middle value when data points are ordered from lowest to highest.

Mode

A descriptive statistic representing the value that occurs most frequently in a dataset.

Variance

A statistical metric measuring how far individual values in a dataset spread out from their mean value.

Outlier

A data point that lies at an abnormal distance from other values in a dataset.

Image Processing

A subset of computer vision focused on processing raw input images to enhance them or prepare them for subsequent computational tasks.

Classification + Localisation

A computer vision task that identifies what single object is present in an image and determines its specific location.

Object Detection

A computer vision task that locates and categorizes multiple real-world objects in an image or video using bounding boxes.

Instance Segmentation

A computer vision task that detects object instances, categorizes them, and assigns a category label to every individual pixel.

Pixel

Short for picture element, it is the smallest unit of visual information arranged in a 2D grid to compose a digital image.

Resolution

The total pixel count in a digital image, expressed either as width by height (e.g., $1280 \times 1024$) or as total megapixels.

Grayscale Image

A digital image where each pixel carries a single byte value ranging from 0 (black) to 255 (white), representing shades of gray.

RGB Image

A color digital image composed of three channels—Red, Green, and Blue—where each pixel contains three 8-bit intensity values ranging from 0 to 255.

Convolution

An element-wise mathematical multiplication of an image pixel matrix and a smaller kernel matrix, followed by summing the results.

Kernel

A small matrix slid across an image during convolution to perform specific image processing effects or feature extractions.

Rectified Linear Unit (ReLU)

An activation layer in a CNN that replaces all negative numbers in a feature map with zero, introducing non-linearity.

Max Pooling

A pooling operation in a CNN that extracts and returns the maximum pixel value from a designated kernel window.

Fully Connected Layer

The final layer of a CNN that takes flattened vector outputs from convolution and pooling steps to calculate classification probabilities.

Lexical Analysis

The initial stage of NLP involving the division of text into structural paragraphs, sentences, and individual words.

Syntactic Analysis (Parsing)

The stage of NLP that checks the grammatical rules and structural correctness of sentences and phrases.

Semantic Analysis

The stage of NLP that checks sentences and phrases for literal meaning and semantic meaningfulness.

Discourse Integration

The stage of NLP that establishes contextual relationships between a sentence and its preceding and succeeding sentences.

Pragmatic Analysis

The final stage of NLP that assesses the real-world context, logical intent, and practical applicability of sentences beyond literal meaning.

Script Bot

A simple, rule-based chatbot programmed around fixed scripts with limited functionality and little or no language processing capabilities.

Smart Bot

An AI-powered chatbot that works on larger databases, learns from data, and uses advanced NLP skills to handle flexible interactions.

Corpus

The entire collection of written textual data across multiple documents used in NLP tasks.

Sentence Segmentation

The text normalization step where an entire text corpus is split into individual sentences.

Tokenization

The text normalization step where sentences are broken down into smaller individual units called tokens (words, numbers, or special characters).

Stop Words

High-frequency words (such as 'and', 'the', 'is') that add little or no semantic value to the context and are removed during text normalization.

Stemming

The text normalization process of stripping affixes from words to obtain their root form, which may not always result in a dictionary-valid word.

Lemmatization

The text normalization process of stripping affixes from words to reduce them to a meaningful root word (lemma).

Bag of Words

An NLP model that extracts text features by generating a unique word vocabulary and tracking word frequency counts, ignoring sentence word order.

Term Frequency (TF)

A metric in NLP representing the number of times a specific word appears within a single document.

Inverse Document Frequency (IDF)

A metric in NLP measuring a word's rarity across all documents, calculated as $\text{IDF}(W) = \log\left(\frac{\text{Total Documents}}{\text{Document Frequency}}\right)$.

TFIDF

A numerical statistic calculated as $\text{TFIDF}(W) = \text{TF}(W) \times \log(\text{IDF}(W))$ to evaluate how important a word is to a document relative to a corpus.