Large Scale Data Mining: Models and Algorithms
EC ENGR XLC 219
This course is part of the UCLA Henry Samueli School of Engineering and Applied Science (HSSEAS) Master of Science in Engineering Online (MSOL) program. It is available only to students pre-approved by HSSEAS. For more information visit msol.ucla.edu.
About this course:EC ENGR 219. Large-Scale Data Mining: Models and Algorithms. (Instructor: Roychowdhury, V.) Lecture, four hours; discussion, one hour; outside study, seven hours. Introduction of variety of scalable data modeling tools, both predictive and causal, from different disciplines. Topics include supervised and unsupervised data modeling tools from machine learning, such as support vector machines, different regression engines, different types of regularization and kernel techniques, deep learning, and Bayesian graphical models. Emphasis on techniques to evaluate relative performance of different methods and their applicability. Includes computer projects that explore entire data analysis and modeling cycle: collecting and cleaning large-scale data, deriving predictive and causal models, and evaluating performance of different models.
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