Applied Machine Learning

3 credits · Undergraduate

Also listed as CS 3262. Fundamentals of machine learning with emphasis on practical applications to data science problems. Supervised learning (linear and logistic regression, decision trees, support vector machines, neural networks, and deep learning), unsupervised learning (feature selection, data clustering, dimensionality reduction); ethical principles and social implications of machine learning. Intended for students other than computer science majors. Serves as repeat credit for CS 3262. Prerequisites: One of DS 1100, CS 1100, CS 2201, CS 2204, CS 2205; one of DS 2100, BME 2400, BSCI 3270, CE 3300, ECON 1500 or 1510, MATH 2810 or 2820 or 2821, PSY 2100, PSY-PC 2110 or 2120, SOC 2100. [3]