AI Glossary File Index

See the [[Main AI Page]] or the [[Master of Philosophy - Main Page]].

Relevant to:

• [[Week 1 - Introduction]]
• [[Week 2 - Introduction]]
• [[Week 3 - Introduction]]
• [[Feminist Chatbot Main Page]]
• [[GOLEM Project Page]]
• [[Economic Indicators List]]

A

• [[A-B Testing]]
• [[Accuracy]]
• [[Action]]
• [[Activation Function]]
• [[Active Learning]]
• [[Adagrad]]
• [[Agent]]
• [[Agglomerative Clustering]]
• [[Ar]]
• [[Area Under The Pr Curve]]
• [[Area Under The Roc Curve]]
• [[Artificial General Intelligence]]
• [[Artificial Intelligence]]
• [[Attribute]]
• [[Auc (Area Under The Roc Curve)]]
• [[Augmented Reality]]
• [[Automation Bias]]
• [[Average Precision]]

B

• [[Backpropagation]]
• [[Bag Of Words]]
• [[Baseline]]
• [[Batch]]
• [[Batch Normalization]]
• [[Batch Size]]
• [[Bayesian Neural Network]]
• [[Bellman Equation]]
• [[Bias (Ethics-Fairness)]]
• [[Bias (Math)]]
• [[Binary Classification]]
• [[Boosting]]
• [[Bounding Box]]
• [[Broadcasting]]
• [[Bucketing]]

C

• [[Calibration Layer]]
• [[Candidate Generation]]
• [[Candidate Sampling]]
• [[Categorical Data]]
• [[Centroid-Based Clustering]]
• [[Centroid]]
• [[Checkpoint]]
• [[Class-Imbalanced Dataset]]
• [[Class]]
• [[Classification Model]]
• [[Classification Threshold]]
• [[Clipping]]
• [[Cloud Tpu]]
• [[Clustering]]
• [[Co-Adaptation]]
• [[Collaborative Filtering]]
• [[Confirmation Bias]]
• [[Confusion Matrix]]
• [[Continuous Feature]]
• [[Convenience Sampling]]
• [[Convergence]]
• [[Convex Function]]
• [[Convex Optimization]]
• [[Convex Set]]
• [[Convolution]]
• [[Convolutional Filter]]
• [[Convolutional Layer]]
• [[Convolutional Neural Network]]
• [[Convolutional Operation]]
• [[Cost]]
• [[Counterfactual Fairness]]
• [[Coverage Bias]]
• [[Crash Blossom]]
• [[Critic]]
• [[Cross-Entropy]]
• [[Cross-Validation]]
• [[Custom Estimator]]

D

• [[Data Analysis]]
• [[Data Augmentation]]
• [[Data Set Or Dataset]]
• [[Dataframe]]
• [[Dataset Api (Tf.Data)]]
• [[Decision Boundary]]
• [[Decision Threshold]]
• [[Decision Tree]]
• [[Deep Model]]
• [[Deep Neural Network]]
• [[Deep Q-Network (Dqn)]]
• [[Demographic Parity]]
• [[Dense Feature]]
• [[Dense Layer]]
• [[Depth]]
• [[Depthwise Separable Convolutional Neural Network (Sepcnn)]]
• [[Device]]
• [[Dimension Reduction]]
• [[Dimensions]]
• [[Discrete Feature]]
• [[Discriminative Model]]
• [[Discriminator]]
• [[Disparate Impact]]
• [[Disparate Treatment]]
• [[Divisive Clustering]]
• [[Downsampling]]
• [[Dqn]]
• [[Dynamic Model]]

E

• [[Eager Execution]]
• [[Early Stopping]]
• [[Embedding Space]]
• [[Embeddings]]
• [[Empirical Risk Minimization (Erm)]]
• [[Ensemble]]
• [[Environment]]
• [[Episode]]
• [[Epoch]]
• [[Epsilon Greedy Policy]]
• [[Equality Of Opportunity]]
• [[Equalized Odds]]
• [[Estimator]]
• [[Example]]
• [[Experience Replay]]
• [[Experimenter’S Bias]]
• [[Exploding Gradient Problem]]

F

• [[Fairness Constraint]]
• [[Fairness Metric]]
• [[False Negative (Fn)]]
• [[False Positive (Fp)]]
• [[False Positive Rate (Fpr)]]
• [[Feature]]
• [[Feature Column (Tf.Feature Column)]]
• [[Feature Cross]]
• [[Feature Engineering]]
• [[Feature Extraction]]
• [[Feature Set]]
• [[Feature Spec]]
• [[Feature Vector]]
• [[Federated Learning]]
• [[Feedback Loop]]
• [[Feedforward Neural Network (Ffn)]]
• [[Few-Shot Learning]]
• [[Fine Tuning]]
• [[Forget Gate]]
• [[Full Softmax]]
• [[Fully Connected Layer]]

G

• [[Gan]]
• [[Generalization]]
• [[Generalization Curve]]
• [[Generalized Linear Model]]
• [[Generative Adversarial Network (Gan)]]
• [[Generative Model]]
• [[Generator]]
• [[Glossary]]
• [[Gradient]]
• [[Gradient Clipping]]
• [[Gradient Descent]]
• [[Graph]]
• [[Graph Execution]]
• [[Greedy Policy]]
• [[Ground Truth]]
• [[Group Attribution Bias]]

H

• [[Hashing]]
• [[Heuristic]]
• [[Hidden Layer]]
• [[Hierarchical Clustering]]
• [[Hinge Loss]]
• [[Holdout Data]]
• [[Hyperparameter]]
• [[Hyperplane]]

I

• [[I.I.D.]]
• [[Image Recognition]]
• [[Imbalanced Dataset]]
• [[Implicit Bias]]
• [[In-Group Bias]]
• [[Incompatibility Of Fairness Metrics]]
• [[Independently And Identically Distributed (I.I.D)]]
• [[Individual Fairness]]
• [[Inference]]
• [[Input Function]]
• [[Input Layer]]
• [[Instance]]
• [[Inter-Rater Agreement]]
• [[Interpretability]]
• [[Intersection Over Union (Iou)]]
• [[Iou]]
• [[Item Matrix]]
• [[Items]]
• [[Iteration]]

K

• [[K-Means]]
• [[K-Median]]
• [[Keras]]
• [[Kernel Support Vector Machines (Ksvms)]]
• [[Keypoints]]

L

• [[L1 Loss]]
• [[L1 Regularization]]
• [[L2 Loss]]
• [[L2 Regularization]]
• [[Label]]
• [[Labeled Example]]
• [[Lambda]]
• [[Landmarks]]
• [[Layer]]
• [[Layers Api (Tf.Layers)]]
• [[Learning Rate]]
• [[Least Squares Regression]]
• [[Linear Model]]
• [[Linear Regression]]
• [[Log-Odds]]
• [[Log Loss]]
• [[Logistic Regression]]
• [[Logits]]
• [[Long Short-Term Memory (Lstm)]]
• [[Loss]]
• [[Loss Curve]]
• [[Loss Surface]]
• [[Lstm]]

M

• [[Machine Learning]]
• [[Majority Class]]
• [[Markov Decision Process .mdp)]]
• [[Markov Property]]
• [[Matplotlib]]
• [[Matrix Factorization]]
• [[Mean Absolute Error (Mae)]]
• [[Mean Squared Error (Mse)]]
• [[Metric]]
• [[Metrics Api (Tf.Metrics)]]
• [[Mini-Batch]]
• [[Mini-Batch Stochastic Gradient Descent (Sgd)]]
• [[Minimax Loss]]
• [[Minority Class]]
• [[Ml]]
• [[Mnist]]
• [[Model]]
• [[Model Capacity]]
• [[Model Function]]
• [[Model Training]]
• [[Momentum]]
• [[Multi-Class Classification]]
• [[Multi-Class Logistic Regression]]
• [[Multinomial Classification]]

N

• [[N-Gram]]
• [[Nan Trap]]
• [[Natural Language Understanding]]
• [[Negative Class]]
• [[Neural Network]]
• [[Neuron]]
• [[Nlu]]
• [[Node (Neural Network)]]
• [[Node (Tensorflow Graph)]]
• [[Noise]]
• [[Non-Response Bias]]
• [[Normalization]]
• [[Numerical Data]]
• [[Numpy]]

O

• [[Objective]]
• [[Objective Function]]
• [[Offline Inference]]
• [[One-Hot Encoding]]
• [[One-Shot Learning]]
• [[One-Vs.-All]]
• [[Online Inference]]
• [[Operation (Op)]]
• [[Optimizer]]
• [[Out-Group Homogeneity Bias]]
• [[Outliers]]
• [[Output Layer]]
• [[Overfitting]]

P

• [[Pandas]]
• [[Parameter]]
• [[Parameter Server (Ps)]]
• [[Parameter Update]]
• [[Partial Derivative]]
• [[Participation Bias]]
• [[Partitioning Strategy]]
• [[Perceptron]]
• [[Performance]]
• [[Perplexity]]
• [[Pipeline]]
• [[Policy]]
• [[Polysemous]]
• [[Pooling]]
• [[Positive Class]]
• [[Post-Processing]]
• [[Pr Auc (Area Under The Pr Curve)]]
• [[Pre-Trained Model]]
• [[Precision-Recall Curve]]
• [[Precision]]
• [[Prediction]]
• [[Prediction Bias]]
• [[Predictive Parity]]
• [[Predictive Rate Parity]]
• [[Premade Estimator]]
• [[Preprocessing]]
• [[Prior Belief]]
• [[Proxy (Sensitive Attributes)]]
• [[Proxy Labels]]

Q

• [[Q-Function]]
• [[Q-Learning]]
• [[Quantile]]
• [[Quantile Bucketing]]
• [[Quantization]]
• [[Queue]]

R

• [[Random Forest]]
• [[Random Policy]]
• [[Rank (Ordinality)]]
• [[Rank (Tensor)]]
• [[Rater]]
• [[Re-Ranking]]
• [[Recall]]
• [[Recommendation System]]
• [[Rectified Linear Unit (Relu)]]
• [[Recurrent Neural Network]]
• [[Regression Model]]
• [[Regularization]]
• [[Regularization Rate]]
• [[Reinforcement Learning (Rl)]]
• [[Replay Buffer]]
• [[Reporting Bias]]
• [[Representation]]
• [[Return]]
• [[Reward]]
• [[Ridge Regularization]]
• [[Rnn]]
• [[Roc (Receiver Operating Characteristic) Curve]]
• [[Root Directory]]
• [[Root Mean Squared Error (Rmse)]]
• [[Rotational Invariance]]

S

• [[Sampling Bias]]
• [[Savedmodel]]
• [[Saver]]
• [[Scalar]]
• [[Scaling]]
• [[Scikit-Learn]]
• [[Scoring]]
• [[Selection Bias]]
• [[Semi-Supervised Learning]]
• [[Sensitive Attribute]]
• [[Sentiment Analysis]]
• [[Sequence Model]]
• [[Serving]]
• [[Session (Tf.Session)]]
• [[Shape (Tensor)]]
• [[Sigmoid Function]]
• [[Similarity Measure]]
• [[Size Invariance]]
• [[Sketching]]
• [[Softmax]]
• [[Sparse Feature]]
• [[Sparse Representation]]
• [[Sparse Vector]]
• [[Sparsity]]
• [[Spatial Pooling]]
• [[Squared Hinge Loss]]
• [[Squared Loss]]
• [[State-Action Value Function]]
• [[State]]
• [[Static Model]]
• [[Stationarity]]
• [[Step]]
• [[Step Size]]
• [[Stochastic Gradient Descent (Sgd)]]
• [[Stride]]
• [[Structural Risk Minimization (Srm)]]
• [[Subsampling]]
• [[Summary]]
• [[Synthetic Feature]]

T

• [[Tabular Q-Learning]]
• [[Target]]
• [[Target Network]]
• [[Temporal Data]]
• [[Tensor]]
• [[Tensor Processing Unit (Tpu)]]
• [[Tensor Rank]]
• [[Tensor Shape]]
• [[Tensor Size]]
• [[Tensorboard]]
• [[Tensorflow]]
• [[Tensorflow Playground]]
• [[Tensorflow Serving]]
• [[Termination Condition]]
• [[Test Set]]
• [[Tf.Example]]
• [[Tf.Keras]]
• [[Time Series Analysis]]
• [[Timestep]]
• [[Tower]]
• [[Tpu]]
• [[Tpu Chip]]
• [[Tpu Device]]
• [[Tpu Master]]
• [[Tpu Node]]
• [[Tpu Pod]]
• [[Tpu Resource]]
• [[Tpu Slice]]
• [[Tpu Type]]
• [[Tpu Worker]]
• [[Training]]
• [[Training Set]]
• [[Trajectory]]
• [[Transfer Learning]]
• [[Translational Invariance]]
• [[Trigram]]
• [[True Negative (Tn)]]
• [[True Positive (Tp)]]
• [[True Positive Rate (Tpr)]]

U

• [[Unawareness (To A Sensitive Attribute)]]
• [[Underfitting]]
• [[Unlabeled Example]]
• [[Unsupervised Machine Learning]]
• [[Upweighting]]
• [[User Matrix]]

V

• [[Validation]]
• [[Validation Set]]
• [[Vanishing Gradient Problem]]

W

• [[Wasserstein Loss]]
• [[Weight]]
• [[Weighted Alternating Least Squares (Wals)]]
• [[Wide Model]]
• [[Width]]

Go back to the [[Master Contents Page]]