Statistical Estimation for Data Science and AI
Key Takeaways
Introduces statistical estimation for data science and AI using methods like maximum likelihood estimation
Original Description
This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings.
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) and the Master of Science in Artificial Intelligence (MS-AI) degrees offered on the Coursera platform. These interdisciplinary degrees bring together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the CU degrees on Coursera are ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.
Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Learn more about the MS-AI program at https://www.coursera.org/degrees/ms-artificial-intelligence-boulder
Logo adapted from photo by Christopher Burns on Unsplash.
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