Johns Hopkins University 3 credits Computer Science
Machine learning is subfield of computer science and artificial intelligence, whose goal is to develop computational systems, methods, and algorithms that can learn from data to improve their performance. This course introduces the foundational concepts of modern Machine Learning, including core principles, popular algorithms and modeling platforms. This will include both supervised learning, which includes popular algorithms like SVMs, logistic regression, boosting and deep learning, as well as unsupervised learning frameworks, which include Expectation Maximization and graphical models. Homework assignments include a heavy programming components, requiring students to implement several machine learning algorithms in a common learning framework. Additionally, analytical homework questions will explore various machine learning concepts, building on the pre-requisites that include probability, linear algebra, multi-variate calculus and basic optimization. Students in the course will develop a learning system for a final project. Prerequisite(s): Students may receive credit for only one of EN.601.475, EN.601.675.;Linear Algebra, Probability, Statistics, Calc III, and Intro Computing/Programming - (AS.110.202 OR AS.110.211) AND (EN.553.211 OR EN.553.310 OR EN.553.311 OR ((EN.553.420 or EN.553.421) AND (EN.553.430 OR EN.553.431)) AND (AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295) AND (EN.500.112 OR EN.500.113 OR EN.500.114 OR EN.601.220 OR AS.250.205 OR EN.580.200 OR EN.601.107))
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