<p>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.</p>

Electrical and Computer Engineering

Known as the birthplace of the Internet and where countless other cutting-edge technologies took their first steps, the school is consistently ranked among the top 10 public engineering schools in the U.S.

Henry Samueli School of Engineering and Applied Science

Electrical and computer engineering continues to make vital contributions to expand access, processing, and control of the vast amount of information and data in the world.

General Catalog

Large-Scale Data Mining: Models and Algorithms

EC ENGR 219

4 units