Machine Learning

About this course:

This course focuses on machine learning, which is concerned with algorithms that transform information into actionable intelligence. This field is made possible due to the rapid and simultaneous evolution of available data, statistical methods, and computing power. Students learn the origins and practical applications of machine learning, how knowledge is defined and represented by computers, and the basic concepts that differentiate machine learning approaches. Machine learning algorithms can be divided into two main groups: supervised learners who are used to construct predictive models and unsupervised learners who are used to build descriptive models. Students learn the classification, numeric predictor, pattern detection, and clustering algorithms. Students learn to train a model, evaluate its performance, and improve its performance. Algorithm uses are illustrated with real-world cases, such as breast cancer diagnosis, spam filtering, identifying bank loan risk, predicting medical expenses, estimating wine quality, identifying groceries frequently purchased together, and finding teen market segments.  International students: due to recent changes in SEVIS requirements for F-1 visa holders, international students must be enrolled in 12 units to be a full-time student. Of those 12 units, two courses must be In-person or Hybrid (In-person).  To remain in status for your F-1 visa you must attend three in-person class sessions for each of your In-person or Hybrid (In-person) UNEX courses.  All three in-person class sessions are mandatory and must be attended. Domestic students: are strongly encouraged to attend the three in-person class sessions. If a domestic student is unable to attend an in-person class session, they must notify their program advisor/representative to ask for alternative arrangements that will allow them to complete the coursework requirements for these three sessions. The dates of these sessions will be identified in your course syllabus.