IST 402 Emerging Topics (Fall 2019)

Emerging Trends in Machine Learning

Course Description and Evalutation Criteria Document


Time Lecture Slides Readings (to be finished before class)
Week 1 - A Brief History of AI and Machine Learning
  1. A Short History of Machine Learning
  2. The History of Artificial Intelligence
Week 2 - Learning from Data & Machine Learning Primer
Week 3 - Advent of Big Data & Deep Learning Revolution
  1. The Human Face of Big Data
Week 4 - Fairness in Machine Learning Case Study: ProPublica VS Northpointe
  1. Machine Bias, ProPublica
  2. Rebuttal by Northpointe on ProPublica's article
  3. ProPublica Rejoinder to Northpointe Rebuttal
Week 5 - Fairness in Machine Learning (Formal Papers)
  1. Sam Corbett-Davies, Sharad Goel. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
Week 6 - Transparency in Machine Learning
  1. Lipton, Zachary. "The Mythos of Model Interpretability"
  2. Solon Barocas, Andrew Selbst. "Big Data's Disparate Impact"
  3. Finale Doshi-Velez, Been Kim. "Towards A Rigorous Science of Interpretable Machine Learning"
  4. Is Artificial Intelligence Permanently Inscrutable?
Week 7 - Transparency in Machine Learning (Application Papers)
  1. Zeng et al., "Interpretable Classification Models for Recidivism Prediction"
  2. Jung et al., "Simple rules for complex decisions"
  3. Caruana et al., "Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission"
Week 8 - Adversarial Machine Learning
  1. The Human Face of Big Data
Week 9 - Ethics in Machine Learning
  1. Beware Online Filter Bubbles
  2. Chancellor et al., "A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media"
  3. Conitzer et al., "Moral Decision Making Frameworks for Artificial Intelligence"

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