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Machine Learning for Science & Society
Syllabus
Basic Facts
Schedule
Learning Outcomes and Evaluation
Notes
Spring 2021
Schedule
Class 1: Introductions
ML and Probability Review
Class 3: ML Pipelines
Class 4: Missing Data: Basic techniques
Missing Data 2:
Missing Data 3
MIssing data
Elastic Net
Multiobjective
Latent Variable Models: GMM & Topic Models
Latent Variable Models:
Semi-supervised learning and noisy labels
Noisy labels as a Bias model
Comparison of Fairness Interventions
Spring 2022
Overview
2022-01-26
Scientific Method & Philosphy of ML
Roles for Computing in Social Change
Missing data Intro
MLSS
Fairness
2022-02-23
2022-02-28
2022-03-02
MLSS
MLSS
Reference
Preparing to present a paper
Posting Notes
Repository
Suggest edit
Open issue
.md
.pdf
Semi-supervised learning and noisy labels
Semi-supervised learning and noisy labels
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Key questions:
How do these reltae?
