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UB Undergraduate Academic Schedule: Spring 2022


This information is updated nightly. Additional information about this course, including real-time course data, prerequisite and corequisite information, is available to current students via the HUB Student Center, which is accessible via MyUB.


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ECO 485SEM - Big Data And Machine Learning
Lecture
Big Data And Machine Learning SKJM Enrollment Information (not real time - data refreshed nightly)
Class #:   23867   Enrollment Capacity:   27
Section:   SKJM   Enrollment Total:   9
Credits:   3.00 credits   Seats Available:   18
Dates:   01/31/2022 - 05/13/2022   Status:   OPEN WITH RESERVES
Days, Time:   T R , 3:30 PM - 4:50 PM
Room:   Park 450 view map
Location:   North Campus      
Reserve Capacities
Description Enrollment Capacity Enrollment Total  
Force Reg: Seats Reserved 1 0  
Comments
Co-taught by Joanne McLaughlin & Sanghoon Kim.
Enrollment Requirements
Prerequisites: Pre-Requisites: ECO 480
  Course Description
This econometrics course introduces the concept of big data and econometric techniques to analyze big data using the tools of machine learning, and provides main ideas and insights on how we can use big data to solve economic problems. The course discusses differences in objectives, techniques, and settings between the machine learning literature in computer science and economics, and introduces some specific methods from the machine learning literature that are emerging as important tools for economists. These methods include supervised learning for regression and classification, ridge regression estimator, the lasso regression estimator, random forest, dimensionality reduction, unsupervised learning methods of clustering, and natural language processing and data scraping as part of data collection. The course introduces students to these methods primarily using Stata and the integration of Stata with Python. The course does not require prior knowledge in computer programming, but it requires standard knowledge in econometrics.
  Instructor(s)
             Mclaughlin look up    
             Kim look up    
  On-line Resources
Other Courses Taught By: Mclaughlin
Other Courses Taught By: Kim