Hey, I'm Briana and I'm applying for PhD programs in fairness, accountability, and transparency in machine learning this semester. I'm also an eager person and want to delve into studies as soon as possible, so I decided to create a repo of resources to study independently.
- UC Berkeley CS294: Fairness in Machine Learning
- Geomblog: A Course on Fairness, Accountability and Transparency in Machine Learning
- Andrew Ng: Machine Learning (Coursera)
- Amazon AWS: Intro to ML
- Machine Learning Mastery: How to Prepare a Photo Caption Dataset for Training a Deep Learning Model
- Computer-Assisted Reporting and Data Journalism Syllabuses
- O'Reilly: How to build and run your first deep learning network
- How to Develop a Deep Learning Caption Generation Model in Python from Scratch
- Hal Daume lll: A Course in Machine Learning
- Neural Networks and Deep Learning
- Ian Goodfellow and Yoshua Bengio and Aaron Courville: Deep Learning
- Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva: Counterfactual Fairness
- Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Scholkopf: Avoiding Discrimination through Causal Reasoning
- Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth: Fair Algorithms for Infinite and Contextual Bandits
- Alex Campolo, Madelyn Sanfilippo, Meredith Whittaker, Kate Crawford: AI Now 2017 Report
- Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, Huang Lin: FairTest: Discovering Unwarranted Associations in Data-Driven Applications
- Solon Barocas & Andrew D. Selbst: Big Data’s Disparate Impact
- John Rawls: Two Concepts of Rules
- Ronald Dworkin: What is Equality? Part 2: Equality of Resources
- Moritz Hardt, Eric Price, Nathan Srebro: Equality of Opportunity in Supervised Learning
- Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian: On the (im)possibility of fairness
- Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, Max Leiserson: Decoupled classifiers for fair and efficient machine learning
- Robin Burke: Multisided Fairness for Recommendation