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How are “Artificial Intelligence” and “Machine Learning” related?

The field of machine learning – originally, we can consider it as a subfield of AI – was concerned with the development of algorithms so that computers can automatically learn (predictive) models from data.

How does ML learn from data

The training data is composed of two parts: features and labels. Using models (usually the classifier is named after the statistical model it uses, but some models don’t use statistical models, like neural nets), the classifier learns what features (or combinations of features) are associated with which labels.

How do we measure a classifier’s success?

This is kind of the bulk of machine learning. There are various techniques for measuring this, but ultimately, it depends on the problem you are answering.

What are the main differences between statistical modeling and machine learning?

In statistical modeling we usually use parametric approaches (e.g., think of linear or logistic regression as the simplest examples of parametric models – we specify the number of parameters upfront), whereas in machine learning, we often use nonparametric approaches, which means that we don’t pre-specify the structure of the model (e.g., K-nearest neighbors, decision trees, kernel SVM, etc.)

What courses are offered in ARMA?

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