MACHINE LEARNING TEST 2025
Artificial intelligence - ANSWER AI is typically defined as the ability of a
machine to perform cognitive functions we associate with human minds, such as
perceiving, reasoning, learning, and problem-solving.
Machine learning - ANSWER Machine- learning algorithms detect patterns
and learn how to make predictions and recommendations by processing data
and experiences, rather than by receiving explicit programming instruction. The
algorithms also adapt in response to new data and experiences to improve
efficacy over time.
Supervised learning - ANSWER An algorithm uses training data and feedback
from humans to learn the relationship of given inputs to a given output (eg, how
the inputs "time of year" and "interest rates" predict housing prices)
Unsupervised learning - ANSWER An algorithm explores input data without
being given
an explicit output variable (eg, explores customer demographic data to identify
patterns)
Reinforcement learning - ANSWER An algorithm learns to perform a task
simply by trying to maximize rewards it receives for its actions (eg, maximizes
points it receives for increasing returns of an investment portfolio)
Linear regression (Supervised) - ANSWER Highly interpretable, standard
method for model- ing the past relationship between independent input variables
and dependent output variables (which can have an infinite number of values) to
help predict future values of the output variables
Logistic regression (Supervised) - ANSWER Extension of linear regression
that's used for classifation tasks, meaning the output variable
is binary (eg, only black or white) rather than continuous (eg, an infinite list of
potential colors)
Linear/quadratic discriminant analysis (Supervised) - ANSWER Upgrades a
logistic regression to deal with nonlinear problems—those in which changes to
