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Machine learning and AI > first 100 ML/AI terms > Flashcards

first 100 ML/AI terms Flashcards

1
Q

Artificial Intelligence (AI)

A

The simulation of human intelligence processes by machines, particularly computer systems.

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2
Q

Machine Learning (ML)

A

A subset of AI that involves the use of algorithms and statistical models to enable computers to learn from data and make decisions without being explicitly programmed.

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3
Q

Deep Learning

A

A subset of ML involving neural networks with many layers that learn complex patterns in data.

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4
Q

Neural Network

A

A network of artificial neurons that mimics the structure of the human brain to process information in a layered fashion.

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5
Q

Supervised Learning

A

A type of ML where the model is trained on labeled data (input-output pairs).

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6
Q

Unsupervised Learning

A

A type of ML that deals with unlabeled data, finding patterns or structures in input data without predefined labels.

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7
Q

Reinforcement Learning

A

An ML technique where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.

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8
Q

Classification

A

A supervised learning task where the model predicts a discrete label for input data.

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9
Q

Regression

A

A supervised learning task where the model predicts a continuous value for input data.

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10
Q

Clustering

A

An unsupervised learning method that groups data points into clusters based on their similarity.

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11
Q

Decision Tree

A

A model that splits data into branches based on feature values, used for classification and regression tasks.

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12
Q

Random Forest

A

An ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes for classification.

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13
Q

Support Vector Machine (SVM)

A

A supervised learning algorithm that finds the hyperplane best separating different classes in the data.

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14
Q

K-Nearest Neighbors (KNN)

A

A simple ML algorithm that classifies data points based on the majority class of their k nearest neighbors.

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15
Q

Gradient Descent

A

An optimization algorithm used to minimize the loss function in various ML models by iteratively adjusting parameters.

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16
Q

Learning Rate

A

A hyperparameter that controls the step size at each iteration of gradient descent.

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17
Q

Overfitting

A

A modeling error that occurs when the model learns the noise in the training data too well, performing poorly on new data.

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18
Q

Underfitting

A

When a model is too simple to capture the underlying pattern in the data, resulting in poor performance on both training and new data.

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19
Q

Bias

A

The error due to overly simplistic assumptions in the learning algorithm, leading to underfitting.

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20
Q

Variance

A

The error due to the model’s sensitivity to small fluctuations in the training set, leading to overfitting.

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21
Q

Hyperparameter

A

A parameter whose value is set before the learning process begins and controls the model’s training process.

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22
Q

Cross-Validation

A

A technique for evaluating ML models by partitioning the data into subsets and training/testing the model multiple times.

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23
Q

Regularization

A

Techniques like L1 (Lasso) and L2 (Ridge) used to prevent overfitting by penalizing large coefficients.

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24
Q

Loss Function

A

A function that measures the discrepancy between the predicted value and the actual value, guiding model training.

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25
Q

Activation Function

A

A function in neural networks that determines the output of a node given an input or set of inputs (e.g., ReLU, Sigmoid).

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26
Q

Backpropagation

A

A method used in neural networks to calculate the gradient of the loss function and update the weights.

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27
Q

Convolutional Neural Network (CNN)

A

A deep learning model commonly used for image recognition tasks, consisting of convolutional layers to extract features.

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28
Q

Recurrent Neural Network (RNN)

A

A neural network with loops, allowing information to persist for sequential data processing like time series or natural language.

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29
Q

Long Short-Term Memory (LSTM)

A

A type of RNN designed to overcome the vanishing gradient problem by introducing memory cells to retain information.

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30
Q

Transfer Learning

A

A technique where a pre-trained model on one task is reused and fine-tuned for another related task.

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31
Q

Ensemble Learning

A

A technique where multiple models are combined to improve the performance of the final model.

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32
Q

Bagging

A

A method in ensemble learning that reduces variance by training multiple models on different random subsets of the training data.

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33
Q

Boosting

A

An ensemble technique that sequentially trains models, with each new model focusing on the errors of the previous ones.

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34
Q

Stochastic Gradient Descent (SGD)

A

A variation of gradient descent where only a random subset of data points is used to update the model parameters.

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35
Q

Epoch

A

One complete pass through the entire training dataset during the learning process.

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36
Q

Batch Size

A

The number of training examples used in one iteration of model training.

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37
Q

Tokenization

A

The process of breaking down text into smaller units, like words or subwords, for processing in NLP tasks.

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38
Q

Word Embedding

A

A dense vector representation of words that captures semantic relationships, such as Word2Vec and GloVe.

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39
Q

Attention Mechanism

A

A technique in deep learning that allows models to focus on specific parts of the input sequence when making predictions.

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40
Q

Transformer

A

An architecture in deep learning that uses self-attention mechanisms and is commonly used for NLP tasks, such as in models like BERT and GPT.

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41
Q

Natural Language Processing (NLP)

A

The field of AI that focuses on the interaction between computers and human language.

42
Q

Bag-of-Words (BoW)

A

A simple NLP model that represents text as an unordered collection of words, disregarding grammar and word order.

43
Q

Naive Bayes

A

A probabilistic classifier based on Bayes’ theorem, assuming independence between features.

44
Q

Principal Component Analysis (PCA)

A

A dimensionality reduction technique that transforms data into a set of orthogonal components.

45
Q

t-Distributed Stochastic Neighbor Embedding (t-SNE)

A

A nonlinear dimensionality reduction technique for visualizing high-dimensional data.

46
Q

Feature Engineering

A

The process of creating new features from existing data to improve model performance.

47
Q

Feature Scaling

A

The process of normalizing or standardizing the range of independent variables or features in the data.

48
Q

One-Hot Encoding

A

A technique to represent categorical variables as binary vectors for use in ML models.

49
Q

Confusion Matrix

A

A table used to describe the performance of a classification model, showing true positives, false positives, true negatives, and false negatives.

50
Q

Precision

A

The ratio of true positive predictions to the total number of positive predictions made by the model.

51
Q

Recall (Sensitivity)

A

The ratio of true positive predictions to the total actual positive cases in the dataset.

52
Q

F1 Score

A

The harmonic mean of precision and recall, providing a single metric for model performance.

53
Q

ROC Curve (Receiver Operating Characteristic)

A

A graphical plot illustrating the performance of a binary classifier as its discrimination threshold is varied.

54
Q

Area Under the Curve (AUC)

A

A single scalar value that summarizes the performance of a classifier, representing the probability of correctly ranking positive and negative instances.

55
Q

K-Means Clustering

A

An unsupervised learning algorithm that partitions data into K clusters based on feature similarity.

56
Q

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

A

A clustering algorithm that groups data based on density, identifying clusters of varying shapes and sizes.

57
Q

Hierarchical Clustering

A

A method of clustering that builds a hierarchy of clusters using either agglomerative (bottom-up) or divisive (top-down) approaches.

58
Q

Outlier Detection

A

The process of identifying data points that are significantly different from the rest of the dataset.

59
Q

Anomaly Detection

A

A method to identify unusual patterns or data points that do not conform to the expected behavior.

60
Q

Autoencoder

A

A type of neural network used to learn efficient representations (encodings) of data, commonly used for dimensionality reduction and anomaly detection.

61
Q

Generative Adversarial Network (GAN)

A

A deep learning model consisting of two neural networks (generator and discriminator) that compete to generate realistic synthetic data.

62
Q

Synthetic Data

A

Artificially generated data that imitates the statistical properties of real-world data, often used for privacy-preserving training.

63
Q

Bias-Variance Tradeoff

A

The balance between a model’s ability to fit the training data (bias) and its ability to generalize to new data (variance).

64
Q

Cold Start Problem

A

A challenge in recommendation systems when there is little or no data for new users or items.

65
Q

Data Augmentation

A

A technique to increase the diversity of training data by applying transformations such as rotation, flipping, or scaling.

66
Q

Dropout

A

A regularization technique in neural networks where randomly selected neurons are ignored during training to prevent overfitting.

67
Q

Early Stopping

A

A method to prevent overfitting by stopping the training process once the model’s performance on a validation set starts to deteriorate.

68
Q

Gradient Vanishing

A

A problem in deep neural networks where gradients become too small, slowing down learning, commonly addressed using techniques like LSTM and ReLU.

69
Q

Softmax

A

An activation function that transforms a vector of values into a probability distribution, used in multi-class classification.

70
Q

Logistic Regression

A

A statistical model for binary classification that estimates the probability of a binary outcome using a logistic function.

71
Q

Multilayer Perceptron (MLP)

A

A class of neural network consisting of multiple layers of nodes in a directed graph.

72
Q

Feature Importance

A

A measure of the contribution of each feature to the model’s predictions.

73
Q

Gradient Boosting

A

An ensemble technique that builds models sequentially, with each new model correcting the errors of the previous ones.

74
Q

XGBoost

A

An optimized implementation of gradient boosting, known for its speed and performance in ML competitions.

75
Q

Hyperparameter Tuning

A

The process of finding the best hyperparameter values for a model using methods like grid search or random search.

76
Q

Learning Curve

A

A plot that shows how a model’s performance improves as it learns from more training data.

77
Q

Min-Max Scaling

A

A feature scaling technique that normalizes data to a fixed range, usually [0, 1].

78
Q

Normalization

A

Adjusting values in data to a common scale without distorting differences in ranges of values.

79
Q

Token

A

A unit of text (e.g., word, subword) used as input for natural language processing models.

80
Q

Bagging

A

Short for Bootstrap Aggregating, an ensemble learning method that uses multiple models trained on random subsets of data to improve performance.

81
Q

Cross-Entropy Loss

A

A loss function commonly used in classification tasks to measure the difference between true labels and predicted probabilities.

82
Q

Batch Normalization

A

A technique to improve the training of deep neural networks by normalizing inputs of each layer.

83
Q

Adam Optimizer

A

An optimization algorithm that adjusts the learning rate based on estimates of the first and second moments of the gradients.

84
Q

BLEU Score (Bilingual Evaluation Understudy)

A

A metric for evaluating the quality of machine-translated text against one or more reference translations.

85
Q

Sparse Matrix

A

A matrix in which most of the elements are zero, often used in text mining and NLP.

86
Q

One-Hot Vector

A

A binary vector used to represent categorical data, where one element is ‘hot’ (1) and all others are ‘cold’ (0).

87
Q

Tokenization

A

The process of breaking down text into smaller units like words, subwords, or characters.

88
Q

Markov Chain

A

A stochastic model describing a sequence of possible events, where each event depends only on the state attained in the previous event.

89
Q

Monte Carlo Simulation

A

A computational technique that uses random sampling to obtain numerical results, often used in probabilistic modeling.

90
Q

Entropy

A

A measure of uncertainty or randomness in information theory, used in decision trees for determining data splits.

91
Q

Information Gain

A

A measure used in decision trees to determine the best feature to split on, based on the reduction in entropy.

92
Q

R-Squared (Coefficient of Determination)

A

A statistical measure that indicates how well a regression model fits the data.

93
Q

Learning Rate Decay

A

A technique that reduces the learning rate over time to stabilize the training process and improve model convergence.

94
Q

Embeddings

A

Low-dimensional, dense vector representations of high-dimensional data, often used in NLP.

95
Q

Perceptron

A

The simplest type of artificial neural network, consisting of a single layer of weights and a threshold activation function.

96
Q

Gradient Clipping

A

A technique to prevent exploding gradients in neural networks by capping gradients to a maximum value.

97
Q

Hinge Loss

A

A loss function used for training SVMs, penalizing points that lie on the wrong side of the decision boundary.

98
Q

Natural Language Generation (NLG)

A

The use of AI to generate human-like text based on input data or patterns.

99
Q

Zero-Shot Learning

A

A model’s ability to make predictions on classes not seen during training by leveraging shared attributes or semantics.

100
Q

Few-Shot Learning

A

An ML approach where models learn to make predictions from a very small number of training examples.

Machine learning and AI (2 decks)

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