Program Overview

Program Overview

The CSE547 program focuses on Machine Learning and statistical techniques for analyzing datasets of massive size and dimensionality. Representations include regularized linear models, graphical models, matrix factorization, sparsity, clustering, and latent factor models. Algorithms include sketching, random projections, hashing, fast nearest-neighbors, large-scale online learning, and parallel (Map-reduce, GraphLab).

Program Details

Catalog Description

Machine Learning and statistical techniques for analyzing datasets of massive size and dimensionality. Representations include regularized linear models, graphical models, matrix factorization, sparsity, clustering, and latent factor models. Algorithms include sketching, random projections, hashing, fast nearest-neighbors, large-scale online learning, and parallel (Map-reduce, GraphLab). Prerequisite: either STAT 535 or CSE 546.

Prerequisites and Credits

• Prerequisites: either STAT 535 or CSE 546
• Credits: 4.0

Recent and Previous Quarters

• Most Recent Quarter:
  • Spring, 2024 (Althoff)
• Previous Quarters:
  • Winter, 2023 (Althoff)
  • Winter, 2022 (Meila)
  • Spring, 2021 (Althoff)
  • Spring, 2020 (Althoff)
  • Spring, 2019 (Althoff)
  • Spring, 2018 (Kakade)
  • Spring, 2017 (Kakade)
  • Spring, 2016 (Kakade)
  • Spring, 2015 (Fox)
  • Winter, 2014 (Fox)