ISYE 6501 EXAM/ LATEST ISYE 6501 FINAL EXAM 2025/2026 TEST BANK AND A NEW
UPDATED STUDY GUIDE ACCURATE EXAM ACTUAL QUESTIONS WITH WELL
ELABORATED ANSWERS (100% CORRECT VERIFIED ANSWERS) NEWEST UPDATED
VERSION 2025/2026 |GUARANTEED SUCCESS A+ (BRAND NEW!)
Algorithm - (ANSWER)a step-by-step procedure designed to carry out a task
Change Detection - (ANSWER)Identifying when a significant change has taken place
Classification - (ANSWER)Separation of data into two or more categories
Classifier - (ANSWER)A boundary that separates data into two or more categories
Cluster - (ANSWER)A group of points that are identified as being similar or near each other
Cluster Center - (ANSWER)In some clustering algorithms (k-means), the central point of a cluster center
(CENTROID)
Clustering - (ANSWER)Separation of points into similar or near groupings. Form of unsupervised
learning.
CUSUM - (ANSWER)change detection method that compares observed distribution mean with a
threshold level of change. Short for Cumulative Sum (also cumsum)
Deep Learning - (ANSWER)Neural Network model with many hidden layers
Dimension - (ANSWER)A feature of the data points.
EM Algorithm - (ANSWER)Expectation Maximization Algorithm. Algorithm with two steps (often
iterated).
1. Finds the function for the expected likelihood of getting the response given current parameters.
2. Finds new parameter values that maximize probability
,ISYE 6501 EXAM/ LATEST ISYE 6501 FINAL EXAM 2025/2026 TEST BANK AND A NEW
UPDATED STUDY GUIDE ACCURATE EXAM ACTUAL QUESTIONS WITH WELL
ELABORATED ANSWERS (100% CORRECT VERIFIED ANSWERS) NEWEST UPDATED
VERSION 2025/2026 |GUARANTEED SUCCESS A+ (BRAND NEW!)
Heuristic - (ANSWER)Algorithm that isn't guaranteed to find the optimal solution
K-means - (ANSWER)Clustering algorithm (unsupervised), that works by defining k centroids and then
mapping each point to the closest centroid.
K-nearest neighbor (K-NN) - (ANSWER)Classification algorithm (supervised), that works by mapping a
data point to the k closest neighbors to it.
Kernel - (ANSWER)A type of function that computes the similarity between two inputs. thanks to what's
sometimes known as the "kernel trick", non-linear classifiers can be found almost as easily as linear
ones. Helps represent higher dimensional data sets.
Learning - (ANSWER)Finding/discovering new patterns in data that can be applied to new data
Machine - (ANSWER)Apparatus that can do something. in ml it often refers to the algorithm and the
computer is run on.
Margin - (ANSWER)for a single point, the distance between the point and the classification boundary; for
a set of points the minimum distance between a point in the set and the classification boundary; Also
called separation.
Machine Learning - (ANSWER)Use of computer algorithms to learn and discover patterns or structure in
data, without being programmed specifically for them.
Misclassified - (ANSWER)To put a data point in the wrong category by a classifier
Neural Network - (ANSWER)A machine learning model that itself is modeled after the workings of
neurons in the brain.
, ISYE 6501 EXAM/ LATEST ISYE 6501 FINAL EXAM 2025/2026 TEST BANK AND A NEW
UPDATED STUDY GUIDE ACCURATE EXAM ACTUAL QUESTIONS WITH WELL
ELABORATED ANSWERS (100% CORRECT VERIFIED ANSWERS) NEWEST UPDATED
VERSION 2025/2026 |GUARANTEED SUCCESS A+ (BRAND NEW!)
Supervised Learning - (ANSWER)Machine learning where the "correct" answer is known for each data
point in the training set.
Support Vector - (ANSWER)In SVM models, the point closest to the classifier, among those in the
category.
Support Vector Machine (SVM) - (ANSWER)Classification Algorithm (Supervised). Uses boundary to
separate data into two or more classes
Unsupervised Learning - (ANSWER)Machine learning where the "correct" answer is not known for the
data points in the training set.
Voronoi Diagram - (ANSWER)Graphical representation of splitting a plane into two or more special
regions with one special point each, where each region's points are closer to the region's special point
than to any other special point (Think K-means but visually represented)
Accuracy - (ANSWER)Fraction of data points correctly classified by the model.
(TP + TN)/(TP + TN + FP + FN)
Confusion Matrix - (ANSWER)Visualization of classification model performance.
Diagnostic odds ratio - (ANSWER)Ratio of the odds that a data point in a certain category is correctly
classified by the model, to the odds that a data point is not in that category is incorrectly classified by
the model; equal to (TP/FN)/(FP/TN) = (TP*TN)/(FP*FN)
Fall out (FPR) - (ANSWER)Fraction of data points not in a certain category that are incorrectly classified
by the model. (FP/TN+FP).
UPDATED STUDY GUIDE ACCURATE EXAM ACTUAL QUESTIONS WITH WELL
ELABORATED ANSWERS (100% CORRECT VERIFIED ANSWERS) NEWEST UPDATED
VERSION 2025/2026 |GUARANTEED SUCCESS A+ (BRAND NEW!)
Algorithm - (ANSWER)a step-by-step procedure designed to carry out a task
Change Detection - (ANSWER)Identifying when a significant change has taken place
Classification - (ANSWER)Separation of data into two or more categories
Classifier - (ANSWER)A boundary that separates data into two or more categories
Cluster - (ANSWER)A group of points that are identified as being similar or near each other
Cluster Center - (ANSWER)In some clustering algorithms (k-means), the central point of a cluster center
(CENTROID)
Clustering - (ANSWER)Separation of points into similar or near groupings. Form of unsupervised
learning.
CUSUM - (ANSWER)change detection method that compares observed distribution mean with a
threshold level of change. Short for Cumulative Sum (also cumsum)
Deep Learning - (ANSWER)Neural Network model with many hidden layers
Dimension - (ANSWER)A feature of the data points.
EM Algorithm - (ANSWER)Expectation Maximization Algorithm. Algorithm with two steps (often
iterated).
1. Finds the function for the expected likelihood of getting the response given current parameters.
2. Finds new parameter values that maximize probability
,ISYE 6501 EXAM/ LATEST ISYE 6501 FINAL EXAM 2025/2026 TEST BANK AND A NEW
UPDATED STUDY GUIDE ACCURATE EXAM ACTUAL QUESTIONS WITH WELL
ELABORATED ANSWERS (100% CORRECT VERIFIED ANSWERS) NEWEST UPDATED
VERSION 2025/2026 |GUARANTEED SUCCESS A+ (BRAND NEW!)
Heuristic - (ANSWER)Algorithm that isn't guaranteed to find the optimal solution
K-means - (ANSWER)Clustering algorithm (unsupervised), that works by defining k centroids and then
mapping each point to the closest centroid.
K-nearest neighbor (K-NN) - (ANSWER)Classification algorithm (supervised), that works by mapping a
data point to the k closest neighbors to it.
Kernel - (ANSWER)A type of function that computes the similarity between two inputs. thanks to what's
sometimes known as the "kernel trick", non-linear classifiers can be found almost as easily as linear
ones. Helps represent higher dimensional data sets.
Learning - (ANSWER)Finding/discovering new patterns in data that can be applied to new data
Machine - (ANSWER)Apparatus that can do something. in ml it often refers to the algorithm and the
computer is run on.
Margin - (ANSWER)for a single point, the distance between the point and the classification boundary; for
a set of points the minimum distance between a point in the set and the classification boundary; Also
called separation.
Machine Learning - (ANSWER)Use of computer algorithms to learn and discover patterns or structure in
data, without being programmed specifically for them.
Misclassified - (ANSWER)To put a data point in the wrong category by a classifier
Neural Network - (ANSWER)A machine learning model that itself is modeled after the workings of
neurons in the brain.
, ISYE 6501 EXAM/ LATEST ISYE 6501 FINAL EXAM 2025/2026 TEST BANK AND A NEW
UPDATED STUDY GUIDE ACCURATE EXAM ACTUAL QUESTIONS WITH WELL
ELABORATED ANSWERS (100% CORRECT VERIFIED ANSWERS) NEWEST UPDATED
VERSION 2025/2026 |GUARANTEED SUCCESS A+ (BRAND NEW!)
Supervised Learning - (ANSWER)Machine learning where the "correct" answer is known for each data
point in the training set.
Support Vector - (ANSWER)In SVM models, the point closest to the classifier, among those in the
category.
Support Vector Machine (SVM) - (ANSWER)Classification Algorithm (Supervised). Uses boundary to
separate data into two or more classes
Unsupervised Learning - (ANSWER)Machine learning where the "correct" answer is not known for the
data points in the training set.
Voronoi Diagram - (ANSWER)Graphical representation of splitting a plane into two or more special
regions with one special point each, where each region's points are closer to the region's special point
than to any other special point (Think K-means but visually represented)
Accuracy - (ANSWER)Fraction of data points correctly classified by the model.
(TP + TN)/(TP + TN + FP + FN)
Confusion Matrix - (ANSWER)Visualization of classification model performance.
Diagnostic odds ratio - (ANSWER)Ratio of the odds that a data point in a certain category is correctly
classified by the model, to the odds that a data point is not in that category is incorrectly classified by
the model; equal to (TP/FN)/(FP/TN) = (TP*TN)/(FP*FN)
Fall out (FPR) - (ANSWER)Fraction of data points not in a certain category that are incorrectly classified
by the model. (FP/TN+FP).
