AI Today Podcast: AI Glossary Series – Confusion Matrix, Accuracy, Precision, F1, Recall, Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curve

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Confusion Matrix, Accuracy, Precision, F1, Recall, Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curve, explain how these terms relate to AI and why it's important to know about them.

Show Notes:

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AI Glossary

Glossary Series: Training Data, Epoch, Batch, Learning Curve

Glossary Series: (Artificial) Neural Networks, Node (Neuron), Layer

Glossary Series: Bias, Weight, Activation Function, Convergence, ReLU

Glossary Series: Perceptron

Glossary Series: Hidden Layer, Deep Learning

Glossary Series: Loss Function, Cost Function & Gradient Descent

Glossary Series: Backpropagation, Learning Rate, Optimizer

Glossary Series: Feed-Forward Neural Network

Glossary Series: OpenAI, GPT, DALL-E, Stable Diffusion

Glossary Series: Natural Language Processing (NLP), NLU, NLG, Speech-to-Text, TTS, Speech Recognition

AI Glossary Series - Machine Learning, Algorithm, Model

AI Glossary Series - Model Tuning and Hyperparameter

AI Glossary Series: Overfitting, Underfitting, Bias, Variance, Bias/Variance Tradeoff

Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary