BUAD 2001 Terms

Data

Raw facts and figures

Information

Data presented in a context so that it can answer a question or support decision-making.

Knowledge

Insight derived from experience and expertise (based on data and information)

Primary Data

data that you have collected for your own use

Secondary Data

data somebody else has collected and made available for others to use

Time series data

values of a specific measurements (subject) taken over a range of time periods

Cross Sectional Data

values collected from a number of subjects during a single time period

Panel Data

consists of a time series for each cross-sectional member

Artificial Intelligence

Computer software that can mimic or improve upon functions that would otherwise require human intelligence.

Neural Networks

A statistical techniques that hunt down and expose patterns, building multilayered relationships that humans can’t detect on their own.

Supervised Machine Learning

• Uses data which are labeled by humans (hence supervised)

• Thanks to labeled data, we can measure accuracy of
  supervised machine learning algorithms

Unsupervised Machine Learning

Discover hidden patterns in data without the need for human
intervention (hence unsupervised)

Machine Learning

A machine learning system is trained, rather than explicitly programed.

Deep Learning

A subset of ML that uses neural network algorithms for predictive applications

Generative AI

Algorithms that generate original content, such as images, text, voice, video, or music, by learning patterns from a dataset

Symbolic AI

Refers to methods in AI research that utilize high-level symbolic representations of problems, logic, and search.

Regression

Predict a continuous numerical value

Classification

Predicts discrete categories or labels

Underfitting

• Model is too simple.

• Performs poorly on training data.

• Performs poorly on testing data.

• Associated with high bias

Overfitting

• Model learns noise or random patterns in the training data.

• Performs very well on training data.

• Performs poorly on testing data.

• Associated with high variance.

Test Data

where the trained model is applied to for assessment of performance, and model comparison

Training Data

From which the model/algorithm “learns”