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PHP 2650 - Statistical Learning and Big Data |
This course introduces modern statistical tools to analyze big data, including three interconnected components: computing tools, statistical machine learning, and scalable algorithms. It introduces the principal techniques: extract and organize data from complex sources, explore patterns, frame statistical problems, build computational algorithms, and disseminate reproducible research. Topics include web data extraction, database management, exploratory data analysis, dimension reduction, convex optimization algorithms, high-dimensional linear/nonlinear models, tree/ensemble methods, and predictive modeling. These techniques are illustrated using big data examples from many scientific disciplines. This course is open to graduate students and advanced undergraduate students pursuing degrees in science, technology, engineering, or mathematics. Students should have taken: either one course from: PHP 2510, PHP 2511, PHP 2550, APMA 2610; OR one course from: APMA 1690, APMA 1720, APMA 1930B, CSCI 0150, CSCI 0170; AND one course from: MATH 0520, MATH 0540. Students may ask permissions from the instructor for waiving this requirement. Students are also required to have some experience with any scripting language.
1.000 Credit hours 1.000 Lecture hours Levels: Graduate, Undergraduate Schedule Types: Primary Meeting Public Health Department Prerequisites: (Graduate level PHP 2510 Minimum Grade of S or Graduate level PHP 2511 Minimum Grade of S or Graduate level APMA 2610 Minimum Grade of S or Graduate level PHP 2550 Minimum Grade of S) or ( (Undergraduate level APMA 1690 Minimum Grade of S or Undergraduate level APMA 1720 Minimum Grade of S or Undergraduate level APMA 1930B Minimum Grade of S or Undergraduate level CSCI 0170 Minimum Grade of S or Undergraduate level CSCI 0150 Minimum Grade of S) and (Undergraduate level MATH 0520 Minimum Grade of S or Undergraduate level MATH 0540 Minimum Grade of S) ) |
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