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ISYE-6501 Exam 1 Questions with 100% correct answers | verified | latest update 2024
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ISYE-6501 Exam 1 Questions with 100% correct answers | verified | latest update 2024
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ISYE-6501 Exam 1Algorithm - ANS-a step-by-step procedure designed to carry out a task
Change Detection - ANS-Identifying when a significant change has taken place
Classification - ANS-Separation of data into two or more categories
Classifier - ANS-A boundary that separates data into two or more categories
Cluster - ANS-A group of points that are identified as being similar or near each other
Cluster Center - ANS-In some clustering algorithms (k-means), the central point of a
cluster center (CENTROID)
Clustering - ANS-Separation of points into similar or near groupings. Form of
unsupervised learning.
CUSUM - ANS-change detection method that compares observed distribution mean
with a threshold level of change. Short for Cumulative Sum (also cumsum)
Deep Learning - ANS-Neural Network model with many hidden layers
Dimension - ANS-A feature of the data points.
EM Algorithm - ANS-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
Heuristic - ANS-Algorithm that isn't guaranteed to find the optimal solution
K-means - ANS-Clustering algorithm (unsupervised), that works by defining k centroids
and then mapping each point to the closest centroid.
K-nearest neighbor (K-NN) - ANS-Classification algorithm (supervised), that works by
mapping a data point to the k closest neighbors to it.
, Kernel - ANS-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 - ANS-Finding/discovering new patterns in data that can be applied to new
data
Machine - ANS-Apparatus that can do something. in ml it often refers to the algorithm
and the computer is run on.
Margin - ANS-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 - ANS-Use of computer algorithms to learn and discover patterns or
structure in data, without being programmed specifically for them.
Misclassified - ANS-To put a data point in the wrong category by a classifier
Neural Network - ANS-A machine learning model that itself is modeled after the
workings of neurons in the brain.
Supervised Learning - ANS-Machine learning where the "correct" answer is known for
each data point in the training set.
Support Vector - ANS-In SVM models, the point closest to the classifier, among those in
the category.
Support Vector Machine (SVM) - ANS-Classification Algorithm (Supervised). Uses
boundary to separate data into two or more classes
Unsupervised Learning - ANS-Machine learning where the "correct" answer is not
known for the data points in the training set.
Voronoi Diagram - ANS-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 - ANS-Fraction of data points correctly classified by the model.
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