Some supplemental readings and references are direct links, others come from the following texts.
- Algorithms by by Sanjoy Dasgupta and Christos Papadimitriou and Umesh Vazirani, 1st Edition, 2008.
- Introduction to Algorithms by by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, 3rd Edition, 2009.
- Algorithmic Game Theory by , , , Cornell University, New York,
- Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations by Yoav Shoham and Kevin Leyton-Brown, 1st Edition, 2009.
- Artificial Intelligence: A Modern Approach by Stuart Russel and Peter Norvig, 3rd Edition, 2010.
8/31 Huffman
- Section 5.2 of Algorithms
- Section 16.3 of Introduction to Algorithms
- MIT Open Courseware [notes]
9/7 Lossy Compression
- Sections 7 and 8 of Guy E. Blelloch’s Introduction to Data Compression
- JPEG at 25: Still Going Strong
9/9 Compression and Streaming
- Section 8.2 of Guy E. Blelloch’s Introduction to Data Compression
- Netflix Tech Blog Articles:
9/14 Routing Algorithms
- Sections 4.1-4.5 of Algorithms
- Section 24 of Introduction to Algorithms
- Sections 3.5-3.6 of Artificial Intelligence: A Modern Approach
9/28 Pagerank and Web Search
- The PageRank Citation Ranking: Bringing Order to the Web by Lawrence Page et al.
- Google: How Search Works
9/30 More on Web Search
- Algorithms of Oppression by Safiya Umoja Noble
10/7 Games and Computing Equilibria
- Sections 1-3 of Algorithmic Game Theory
- Mastering the game of Go with deep neural networks and tree search by Silver et al.
10/12 Recommender
- Amazon.com recommendations: item-to-item collaborative filtering by G. Linden, B. Smith, and J. York.
- Section 9.3 of Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman
- Recommender Systems: Beyond Matrix Completion by Dietmar Jannach, Paul Resnick, Alexander Tuzhilin, Markus Zanker
-
The Netflix Recommender System: Algorithms, Business Value, and Innovation by Carlos A. Gomez-Uribe and Neil Hunt
10/14 More Recommender
- Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality by Sariel Har-Peled, Piotr Indyk, and Rajeev Motwani
- Section 9.4 of Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman
- How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility by Allison J.B. Chaney, Brandon M. Stewart, and Barbara E. Engelhardt
10/21 Hashing Big Data
- Section 11 of Introduction to Algorithms (basic hashing)
- An Improved Data Stream Summary: The Count-Min Sketch and its Applications by Cormode and Muthukrishnan
10/26 Consistent Hashing
- Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web, by Karger et al.
- Dynamo: amazon’s highly available key-value store, by DeCandia et al.
10/28 Algorithms for Cryptography
- Algorithms, Sections 1.2-1.4
- A Graduate Course in Applied Cryptography by Dan Boneh and Victor Shoup, Sections 2.1, 3.1-3.3, 3.9
11/2 Stable Matching
- Algorithmic Game Theory, Sections 10.3 and 10.4
- The Economics of Matching by Roth
- School Choice: A Mechanism Design Approach by Abdulkadiroğlu and Sönmez
- School Choice in Chile by Correa et al.
- Strategic Behavior in a Strategy-Proof Environment by Hassidim et al.
11/9 Online Matching for Ads
- On-line Bipartite Matching Made Simple by Birnbaum and Mathieu
- AdWords and Generalized Online Matching by Mehta et al.
- Discrimination in Online Ad Delivery: Google ads, black names and white names, racial discrimination, and click advertising by Sweeney
-
Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination by Datta et al.
11/11 Discrete Optimization
- Algorithms, Sections 9.1 and 9.2
- Mixed-Integer Programming (MIP) – A Primer on the Basics by Gurobi
11/16 Machine Learning and Neural Networks
- Deep Learning, Sections 6 and 9, by Goodfellow, Benigo, and Courville
11/18 Gradient Descent
- Deep Learning, Section 8, by Goodfellow, Benigo, and Courville
- Convex Optimization, Section 9.3, by Boyd and Vandenberghe
11/23 Bias and Fairness
- Machine Bias, by Julia Angwin et al. of ProPublica
- Fairness and Machine Learning, Sections 1-2, by Barocas, Hardt, and Narayanan
- Big Data: A Report on Algorithmic Systems, Opportunities, and Civil Rights, from the Executive Office of the White House (the 2016 Obama Administration)
