GLOSSARY FOR ISYE 6501: INTRODUCTION TO
ANALYTICS MODELING QUESTIONS | TESTED AND
PROVEN ANSWERS | LATEST UPDATE 2024/2025
100% (GRADE A+)
Algorithm
Ans>> Step-by-step procedure designed to carry out a task.
Change detection
Ans>> Identifying when a significant change has taken place in a process.
Classification
Ans>> The separation of data into two or more categories, or (a point's classification) the
category a data point is put into.
Classifier
Ans>> A boundary that separates the data into two or more categories. Also (more generally)
an algorithm that performs classification.
1
,Cluster
Ans>> A group of points identified as near/similar to each other.
Cluster center
Ans>> In some clustering algorithms (like 𝑘𝑘-means clustering), the central point (often the
centroid) of a cluster of data points.
Clustering
Ans>> Separation of data points into groups ("clusters") based on nearness/similarity to each
other. A common form of unsupervised learning.
CUSUM
Ans>> Change detection method that compares observed distribution mean with a threshold
level of change. Short for 'cumulative sum'.
Deep learning
Ans>> Neural network-type model with many hidden layers.
2
,Dimension
Ans>> A feature of the data points (for example, height or credit score).
EM algorithm
Ans>> Expectation-maximization algorithm.
Expectation-maximization algorithm (EM algorithm)
Ans>> General description of an algorithm with two steps (often iterated), one that finds the
function for the expected likelihood of getting the response given current parameters, and one
that finds new parameter values to maximize that probability.
Heuristic
Ans>> Algorithm that is not guaranteed to find the absolute best (optimal) solution.
𝑘𝑘-means algorithm
Ans>> Clustering algorithm that defines 𝑘𝑘 clusters of data points, each corresponding to one of
𝑘𝑘 cluster centers selected by the algorithm.
3
, 𝑘𝑘-Nearest-Neighbor (KNN)
Ans>> Classification algorithm that defines a data point's category as a function of the nearest
𝑘𝑘 data points to it.
Kernel
Ans>> A type of function that computes the similarity between two inputs; thanks to what's
(really!) sometimes known as the 'kernel trick', nonlinear classifiers can be found almost as easily
as linear ones.
Learning
Ans>> Finding/discovering patterns (or rules) in data, often that can be applied to new data.
Machine
Ans>> Apparatus that can do something; in 'machine learning', it often refers to both an
algorithm and the computer it's run on.
Margin
4
ANALYTICS MODELING QUESTIONS | TESTED AND
PROVEN ANSWERS | LATEST UPDATE 2024/2025
100% (GRADE A+)
Algorithm
Ans>> Step-by-step procedure designed to carry out a task.
Change detection
Ans>> Identifying when a significant change has taken place in a process.
Classification
Ans>> The separation of data into two or more categories, or (a point's classification) the
category a data point is put into.
Classifier
Ans>> A boundary that separates the data into two or more categories. Also (more generally)
an algorithm that performs classification.
1
,Cluster
Ans>> A group of points identified as near/similar to each other.
Cluster center
Ans>> In some clustering algorithms (like 𝑘𝑘-means clustering), the central point (often the
centroid) of a cluster of data points.
Clustering
Ans>> Separation of data points into groups ("clusters") based on nearness/similarity to each
other. A common form of unsupervised learning.
CUSUM
Ans>> Change detection method that compares observed distribution mean with a threshold
level of change. Short for 'cumulative sum'.
Deep learning
Ans>> Neural network-type model with many hidden layers.
2
,Dimension
Ans>> A feature of the data points (for example, height or credit score).
EM algorithm
Ans>> Expectation-maximization algorithm.
Expectation-maximization algorithm (EM algorithm)
Ans>> General description of an algorithm with two steps (often iterated), one that finds the
function for the expected likelihood of getting the response given current parameters, and one
that finds new parameter values to maximize that probability.
Heuristic
Ans>> Algorithm that is not guaranteed to find the absolute best (optimal) solution.
𝑘𝑘-means algorithm
Ans>> Clustering algorithm that defines 𝑘𝑘 clusters of data points, each corresponding to one of
𝑘𝑘 cluster centers selected by the algorithm.
3
, 𝑘𝑘-Nearest-Neighbor (KNN)
Ans>> Classification algorithm that defines a data point's category as a function of the nearest
𝑘𝑘 data points to it.
Kernel
Ans>> A type of function that computes the similarity between two inputs; thanks to what's
(really!) sometimes known as the 'kernel trick', nonlinear classifiers can be found almost as easily
as linear ones.
Learning
Ans>> Finding/discovering patterns (or rules) in data, often that can be applied to new data.
Machine
Ans>> Apparatus that can do something; in 'machine learning', it often refers to both an
algorithm and the computer it's run on.
Margin
4
