EC ENGR 219
Large-Scale Data Mining: Models and Algorithms
Description: 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. Letter grading.
Units: 4.0
Units: 4.0
Most Helpful Review
Winter 2024 - Class is graded on just four group projects which is nice, but they are a decent amount of work. Lectures felt really disorganized, so I ended up not going after a few but was fine in doing the projects. It was nice that I got some hands-on work with the projects, but I didn't learn much theory from the lectures.
Winter 2024 - Class is graded on just four group projects which is nice, but they are a decent amount of work. Lectures felt really disorganized, so I ended up not going after a few but was fine in doing the projects. It was nice that I got some hands-on work with the projects, but I didn't learn much theory from the lectures.