age 1 of 70
ISYE 6501 MIDTERM 1 EXAM STUDY GUIDE 2025/2026
ACCURATE QUESTIONS AND CORRECT DETAILED
ANSWERS WITH RATIONALES || 100% GUARANTEED
PASS <BRAND NEW VERSION>
Chance Constraint ......ANSWER........A probability-based
constraint. For example, a standard linear constraint might be
𝐴x≤𝑏. A similar chance constraint might be Pr (𝐴x≤𝑏)≥0.95
Collective outlier ......ANSWER........A set of data points that is
(uncommonly) different from others - for example, a missing
heartbeat in an electrocardiogram; we don't know exactly which
millisecond it should've happened in, but collectively there's a set
of milliseconds that it's missing from.
Concave Function ......ANSWER........A function f() where for every
two points 𝑥 and 𝑦, 𝑓(𝑐x+ (1−𝑐)𝑦) ≥ 𝑐𝑓(𝑥) + (1−𝑐)𝑓(𝑦) for all 𝑐
,age 2 of 70
between 0 and 1. In two dimensions, this means if the points
(𝑥,𝑓(𝑥)) and (𝑦,𝑓(𝑦)) are connected with a straight line, the line is
always below [or equal to] the function's curve between those
two points. If 𝑓() is concave, then −𝑓() is convex.
Change Detection ......ANSWER........Identifying when a significant
change has taken place in a process.
Classification ......ANSWER........The separation of data into two
or more categories, or (a point's classification) the category a
data point is put into.
Classification tree ......ANSWER........Tree-based method for
classification. After branching to split the data, each subset is
analyzed with its own classification model.
,age 3 of 70
Classifier ......ANSWER........A boundary that separates the data
into two or more categories. Also (more generally) an algorithm
that performs classification.
Clique ......ANSWER........A set of nodes where each pair is
connected by an arc.
Cluster ......ANSWER........A group of points identified as
near/similar to each other.
Cluster Center ......ANSWER........In some clustering algorithms
(like 𝑘𝑘-means clustering), the central point (often the centroid)
of a cluster of data points.
Clustering ......ANSWER........Separation of data points into
groups ("clusters") based on nearness/similarity to each other. A
common form of unsupervised learning.
, age 4 of 70
concordance index ......ANSWER........Area under the ROC curve;
an estimate of the classification model's accuracy. Also called
AUC.
Confusion matrix ......ANSWER........Visualization of classification
model performance.
Constant ......ANSWER........A number that remains the same.
constraint ......ANSWER........Part of an optimization model that
describes a restriction on the solution (the values of the
variables).
Contextual outlier ......ANSWER........A data point that is
(uncommonly) far from other data points related to it - for
example, in Atlanta, a 90-degree (Fahrenheit) day in winter is
an outlier, but a 90-degree day in summer is not.
ISYE 6501 MIDTERM 1 EXAM STUDY GUIDE 2025/2026
ACCURATE QUESTIONS AND CORRECT DETAILED
ANSWERS WITH RATIONALES || 100% GUARANTEED
PASS <BRAND NEW VERSION>
Chance Constraint ......ANSWER........A probability-based
constraint. For example, a standard linear constraint might be
𝐴x≤𝑏. A similar chance constraint might be Pr (𝐴x≤𝑏)≥0.95
Collective outlier ......ANSWER........A set of data points that is
(uncommonly) different from others - for example, a missing
heartbeat in an electrocardiogram; we don't know exactly which
millisecond it should've happened in, but collectively there's a set
of milliseconds that it's missing from.
Concave Function ......ANSWER........A function f() where for every
two points 𝑥 and 𝑦, 𝑓(𝑐x+ (1−𝑐)𝑦) ≥ 𝑐𝑓(𝑥) + (1−𝑐)𝑓(𝑦) for all 𝑐
,age 2 of 70
between 0 and 1. In two dimensions, this means if the points
(𝑥,𝑓(𝑥)) and (𝑦,𝑓(𝑦)) are connected with a straight line, the line is
always below [or equal to] the function's curve between those
two points. If 𝑓() is concave, then −𝑓() is convex.
Change Detection ......ANSWER........Identifying when a significant
change has taken place in a process.
Classification ......ANSWER........The separation of data into two
or more categories, or (a point's classification) the category a
data point is put into.
Classification tree ......ANSWER........Tree-based method for
classification. After branching to split the data, each subset is
analyzed with its own classification model.
,age 3 of 70
Classifier ......ANSWER........A boundary that separates the data
into two or more categories. Also (more generally) an algorithm
that performs classification.
Clique ......ANSWER........A set of nodes where each pair is
connected by an arc.
Cluster ......ANSWER........A group of points identified as
near/similar to each other.
Cluster Center ......ANSWER........In some clustering algorithms
(like 𝑘𝑘-means clustering), the central point (often the centroid)
of a cluster of data points.
Clustering ......ANSWER........Separation of data points into
groups ("clusters") based on nearness/similarity to each other. A
common form of unsupervised learning.
, age 4 of 70
concordance index ......ANSWER........Area under the ROC curve;
an estimate of the classification model's accuracy. Also called
AUC.
Confusion matrix ......ANSWER........Visualization of classification
model performance.
Constant ......ANSWER........A number that remains the same.
constraint ......ANSWER........Part of an optimization model that
describes a restriction on the solution (the values of the
variables).
Contextual outlier ......ANSWER........A data point that is
(uncommonly) far from other data points related to it - for
example, in Atlanta, a 90-degree (Fahrenheit) day in winter is
an outlier, but a 90-degree day in summer is not.
