ISYE-6501 Exam 1 Top Questions and answers, 100% Accurate. Rated A+

ISYE-6501 Exam 1 Top Questions and answers, 100% Accurate. Rated A+ Algorithm - -a step-by-step procedure designed to carry out a task Change Detection - -Identifying when a significant change has taken place Classification - -Separation of data into two or more categories Classifier - -A boundary that separates data into two or more categories Cluster - -A group of points that are identified as being similar or near each other Cluster Center - -In some clustering algorithms (k-means), the central point of a cluster center (CENTROID) Clustering - -Separation of points into similar or near groupings. Form of unsupervised learning. CUSUM - -change detection method that compares observed distribution mean with a threshold level of change. Short for Cumulative Sum (also cumsum) Deep Learning - -Neural Network model with many hidden layers Dimension - -A feature of the data points. EM Algorithm - -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 - -Algorithm that isn't guaranteed to find the optimal solution K-means - -Clustering algorithm (unsupervised), that works by defining k centroids and then mapping each point to the closest centroid. K-nearest neighbor (K-NN) - -Classification algorithm (supervised), that works by mapping a data point to the k closest neighbors to it.