ISYE-6501 Exam 1 QUESTIONS & CORRECT ANSWERS

ISYE-6501 Exam 1 QUESTIONS & CORRECT ANSWERS 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 (kmeans), 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 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. 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, t