Optimal Iterative Algorithms for Problems With Random Data
Offered By: Simons Institute via YouTube
Course Description
Overview
Delve into advanced concepts of optimal iterative algorithms for problems with random data in this continuation lecture by Andrea Montanari from Stanford University. Explore key topics in computational complexity of statistical inference as part of the Computational Complexity of Statistical Inference Boot Camp. Gain insights into cutting-edge research and methodologies for solving complex problems involving random data structures. Enhance your understanding of statistical inference techniques and their applications in various computational scenarios.
Syllabus
Optimal Iterative Algorithms for Problems With Random Data (continued)
Taught by
Simons Institute
Related Courses
Statistics in MedicineStanford University via Stanford OpenEdx Introduction to Statistics: Inference
University of California, Berkeley via edX Probability - The Science of Uncertainty and Data
Massachusetts Institute of Technology via edX Statistical Inference
Johns Hopkins University via Coursera Explore Statistics with R
Karolinska Institutet via edX