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. The machine learning language, R, is a cross-platform, zero-cost statistical programming environment, which offers a powerful but easy-to-learn set of tools that can assist students with finding data insights. 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 that are used to construct predictive models, and unsupervised learners that 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.
PrerequisitesPrior knowledge in R, COM SCI X 450.1 Introduction to Data Science, or consent of instructor.
Applies Towards the Following Certificates
- Applications Programming : Electives
- Applications Programming in C# .NET : Electives
- Data Science : Required
- Database Management : Electives
- Linux/Unix : Electives
- Operating System Administration : Electives
- Study Abroad at UCLA Program : Required
- Systems Analysis : Electives
- Web Technology : Electives