6.401/6.481 Introduction to Statistical Data Analysis (NEW)

SHARE:

Undergraduate Level (AUS2), meets with 6.481 grad level
Prereqs: 6.0001 and (6.008, 6.041, or 18.600)
Units: 4-0-8
Instructors: Prof. Yury Polyanskiy (yury@mit.edu)
Schedule: MW2:30-4, virtual instruction
 
Description
 
This subject qualifies as an Artificial Intelligence concentration subject.
 
Introduction to the central concepts and methods of data science with an emphasis on statistical grounding and modern computational capabilities. Covers principles involved in extracting information from data for the purpose of making predictions or decisions, including data exploration, feature selection, model fitting, and performance assessment. Topics include learning of distributions, hypothesis testing (including multiple comparison procedures), linear and nonlinear regression and prediction, classification, time series, uncertainty quantification, model validation, causal inference, optimization, and decisions. Computational case studies and projects drawn from applications in finance, sports, engineering, and machine learning life sciences. Students taking graduate version complete additional assignments. Recommended prerequisite: 18.06.
 
More information on how this subject will be taught at https://eecs.scripts.mit.edu/eduportal/__How_Courses_Will_Be_Taught_Online_or_Oncampus__/S/2021/#6.401 (6.481)