Please note that the following schedule is tentative and subject to change.
| Week | Date | TOPIC | REFERENCES | DUE |
|---|---|---|---|---|
| 1 | W 8/19 | Intro to Algorithms in the Real World | ||
| F 8/21 | Huffman Compression | Section 5.2 of [DPV] MIT Open Courseware [notes] Kamesh [lecture notes] | Course Survey | |
| 2 | W 8/26 | Lossy Compression | Guy Blelloch [notes] | |
| F 8/28 | Sampling: Random Bits and Big Data | Roughgarden and Valiant lecture [notes] Vitter [paper] Roughgarden and Valiant lecture [notes] Levin and Peres Markov chain [book] | HW 1 | |
| 3 | W 9/2 | Hashing Big Data | Roughgarden and Valiant [lecture notes] Cormode and Muthukrishnan [paper] | |
| F 9/4 | Mini-Project Workshop | HW 2 | ||
| 4 | W 9/9 | Consistent Hashing and Distributed Systems | Karger et al. [paper] Roughgarden and Valiant lecture [notes] | |
| F 9/11 | Algorithms for Cryptography and the Internet | Section 1.4 of [DPV] Herlihy Blockchain [article] Section 1 of [DPV] Maggs Bitcoin [slides] Original Nakamoto Bitcoin [paper] | Mini-Project | |
| 5 | W 9/16 | Stable Matching and School Choice | Section 10.4 of [NRTV] Roughgarden [notes] | |
| F 9/18 | Online Matching and Ad Auctions | Mehta et al. [paper] Birnbaum and Mathieu [paper] | HW 3 | |
| 6 | W 9/23 | Pagerank and Web Search | Kamesh lecture [notes] Levin and Peres Markov chain [book] | |
| F 9/25 | Project Proposal Workshop | HW 4 | ||
| 7 | W 9/30 | More on Pagerank and Web Search | Kamesh lecture [notes] Roughgarden and Valiant lecture [notes1 ], [notes2 ] Original PageRank [paper] | |
| F 10/2 | "Fall Break" | Project Proposal | ||
| 8 | W 10/7 | Nearest Neighbor Search and Recommender Systems | Recommender Systems Review [article] Netflix Recommender system [paper] Andoni and Indyk Nearest Neighbor Search [article] Kamesh locality sensitive hashing lecture [ notes1], [notes2] | |
| F 10/9 | Machine Learning, Neural Networks, Gradient Descent | Duke ML Coursera [course] Goodfellow, Benigo, Courville deep learning [book] Pytorch [tutorial] | ||
| 9 | W 10/14 | Image and Speech Recognition with Neural Networks | See 10/9 | HW 5 |
| F 10/16 | Back Propagation and Convolutional Neural Networks | See 10/9 | ||
| 10 | W 10/21 | Algorithmic Bias and Fairness in Machine Learning | Propublica [article] Barocas, Hardt, and Narayanan [book] White House [report] Professor Rudin Interpretability [paper] | |
| F 10/23 | Branch and Bound Discrete Optimization | Section 9.1 of [DPV] | Project Prototype | |
| 11 | W 10/28 | More on Branch and Bound Discrete Optimization | ||
| F 10/30 | Routing Algorithms: Dijkstra's to A* | |||
| 12 | W 11/4 | Project work day: No meeting | ||
| F 11/6 | Nash Equilibria and Monte Carlo Tree Search for Game Playing | Sections 2-4 of [NRTV] Zinkevich et al. 2007 NIPS [paper] Reinforcement Learning: An Introduction (Sutton & Barto) Silver et al. Nature 2016 [paper] | ||
| 13 | W 11/11 | Project Presentations | Project Presentation | |
| F 11/13 | Wrap Up | Peer Assessment Project Report HW6 [optional] |
