AI and Machine Learning

k-nearest neighbors (k-NN)

A classification method that classifies an observation based on the class of the k observations most similar or nearest to it.

Euclidean distance

A method of distance measurement using the straight line mileage between two places.

artificial intelligence

Artificial intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals.

natural language processing

Techniques for processing human languages to enable a computer to understand their meaning. text classification information retrieval name entity recognition - detecting Queen Elizabeth is a person

Overfitting

The process of fitting a model too closely to the training data for the model to be effective on other data.

Logistic Regression

a method of classification: the model outputs the probability of a categorical target variable Y belonging to a certain class.

linear regression

a statistical method used to fit a linear model to a given data set

Supervised Learning

Agent observes input-output pairs & learns to map input to output

Regression

Predict a continuous numerical value. How much will that house sell for?

non-parametric learners

non-parametric learners do not have a model structure specified a priori. We don't speculate about the form of the function f that we're trying to learn before training the model, as we did previously with linear regression. Instead, the model structure is purely determined from the data.

Support vector machines (SVMs)

predictive model approach for classifying things based on geometry; They attempt to find a plane that separates the two classes cleanly. When this isn't possible, we either soften the definition of "separate," or we throw the data into higher dimensions so that we can cleanly separate the data.

Bias

the amount of error introduced by approximating real-world phenomena with a simplified model; goal is to minimize bias, high bias = underfit

gradient descent

A technique to minimize loss by computing the gradients of loss with respect to the model's parameters, conditioned on training data. Informally, gradient descent iteratively adjusts parameters, gradually finding the best combination of weights and bias to minimize loss.

Unsupervised Learning

A type of model creation, derived from the field of machine learning, that does not have a defined target variable.

artificial narrow intelligence (ANI)

AI which can effectively perform a narrowly defined task.

Classifications

assign a label. Is this a picture of a cat or a dog?

Variance

how much your model's test error changes based on variation in the training data. It reflects the model's sensitivity to the idiosyncrasies of the data set it was trained on; goal is to minimize variance, high variance = overfit

Computer vision

Class of AI that can take images it video as an input and output object detection (labeling), face recognition, object tracking.

Machine Learning

Machine learning is a subfield of artificial intelligence. Its goal is to enable computers to learn on their own. A machine's learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models.