References

Every work cited across the course notes, in one place. Individual lectures list the references relevant to them at the end of the lecture.

Adhikari, Ani, John DeNero, and David Wagner. 2022. Computational and Inferential Thinking: The Foundations of Data Science. 2nd ed. UC Berkeley. https://inferentialthinking.com/.

Angrist, Joshua D., and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.

Angrist, Joshua D., and Jörn-Steffen Pischke. 2014. Mastering ’Metrics: The Path from Cause to Effect. Princeton University Press.

Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer Normalization. https://arxiv.org/abs/1607.06450.

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014. “Neural Machine Translation by Jointly Learning to Align and Translate.” arXiv Preprint arXiv:1409.0473. http://arxiv.org/abs/1409.0473.

Bengio, Yoshua, Réjean Ducharme, and Pascal Vincent. 2000. “A Neural Probabilistic Language Model.” Advances in Neural Information Processing Systems 13.

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Blei, David M., Andrew Y. Ng, and Michael I. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3: 993–1022. https://jmlr.org/papers/v3/blei03a.html.

Breiman, Leo. 2001. “Statistical Modeling: The Two Cultures.” Statistical Science 16 (3): 199–231. https://doi.org/10.1214/ss/1009213726.

Brown, Tom B, Benjamin Mann, Nick Ryder, et al. 2020. “Language Models Are Few-Shot Learners.” arXiv Preprint arXiv:2005.14165, ahead of print. https://doi.org/10.48550/arXiv.2005.14165.

Card, David, and Alan B. Krueger. 1994. “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania.” American Economic Review 84 (4): 772–93.

Charig, C. R., D. R. Webb, S. R. Payne, and J. E. Wickham. 1986. “Comparison of Treatment of Renal Calculi by Open Surgery, Percutaneous Nephrolithotomy, and Extracorporeal Shockwave Lithotripsy.” British Medical Journal (Clinical Research Ed.) 292 (6524): 879–82. https://doi.org/10.1136/bmj.292.6524.879.

Chase, Harrison. 2022. LangChain. Https://github.com/langchain-ai/langchain.

Cheng, H.-T. et al. 2016. “Wide & Deep Learning for Recommender Systems.” arXiv Preprint arXiv:1606.07792, June. http://arxiv.org/abs/1606.07792.

Cinelli, Carlos, Andrew Forney, and Judea Pearl. 2024. “A Crash Course in Good and Bad Controls.” Sociological Methods & Research 53: 1071–104. https://doi.org/10.1177/00491241221099552.

Cleveland, R. B., W. S. Cleveland, J. E. McRae, and I. J. Terpenning. 1990. “STL: A Seasonal-Trend Decomposition Procedure Based on Loess.” Journal of Official Statistics 6 (1): 3–33. http://bit.ly/stl1990.

Cunningham, Scott. 2021. Causal Inference: The Mixtape. Yale University Press. https://mixtape.scunning.com/.

Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” arXiv Preprint arXiv:1810.04805, ahead of print. https://doi.org/10.48550/arXiv.1810.04805.

Donoho, David. 2017. “50 Years of Data Science.” Journal of Computational and Graphical Statistics 26 (4): 745–66. https://doi.org/10.1080/10618600.2017.1384734.

Giorgino, Toni. 2009. “Computing and Visualizing Dynamic Time Warping Alignments in r: The Dtw Package.” Journal of Statistical Software 31 (7): 1–24. https://doi.org/10.18637/jss.v031.i07.

Grootendorst, Maarten. 2022. “BERTopic: Neural Topic Modeling with a Class-Based TF-IDF Procedure.” arXiv Preprint arXiv:2203.05794. https://arxiv.org/abs/2203.05794.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning. 2nd ed. Springer. https://hastie.su.domains/ElemStatLearn/.

Hernán, Miguel A., and James M. Robins. 2020. Causal Inference: What If. Chapman & Hall/CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/.

Hyndman, R. J., and G. Athanasopoulos. 2021. Forecasting: Principles and Practice, 3rd Edition. OTexts. https://otexts.com/fpp3/.

Imbens, Guido W., and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.

Jurafsky, Daniel, and James H. Martin. 2024. Speech and Language Processing. 3rd ed. https://web.stanford.edu/~jurafsky/slpdraft/9.pdf.

Karpathy, Andrej. 2015. The Unreasonable Effectiveness of Recurrent Neural Networks. https://karpathy.github.io/2015/05/21/rnn-effectiveness/.

Knuth, Donald E. 1984. “Literate Programming.” Comput. J. (USA) 27 (2): 97–111. https://doi.org/10.1093/comjnl/27.2.97.

Koren, Yehuda. 2009. “Collaborative Filtering with Temporal Dynamics.” Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 42: 447–56. https://dl.acm.org/doi/10.1145/1557019.1557072.

Koren, Yehuda, Robert Bell, and Chris Volinsky. 2009. “Matrix Factorization Techniques for Recommender Systems.” Computer 42 (8): 30–37. https://ieeexplore.ieee.org/document/5197422.

Lay, David C., Steven R. Lay, and Judi J. McDonald. 2021. Linear Algebra and Its Applications. 6th ed. Pearson.

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Linden, Greg, Brent Smith, and Jeremy York. 2003. “Amazon.com Recommendations: Item-to-Item Collaborative Filtering.” IEEE Internet Computing 7 (1): 76–80. https://doi.org/10.1109/MIC.2003.1167344.

McAuley, Julian John, and Jure Leskovec. 2013. “From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise Through Online Reviews.” Proceedings of the 22nd International Conference on World Wide Web (New York, NY, USA), WWW ’13, 897–908. https://doi.org/10.1145/2488388.2488466.

McKinney, Wes. 2022. Python for Data Analysis. 3rd ed. O’Reilly Media. https://wesmckinney.com/book/.

Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. “Efficient Estimation of Word Representations in Vector Space.” Proceedings of Workshop at ICLR. https://arxiv.org/abs/1301.3781.

Naumov, Maxim et al. 2019. “Deep Learning Recommendation Model for Personalization and Recommendation Systems.” arXiv Preprint arXiv:1906.00091, May. http://arxiv.org/abs/1906.00091.

Novembre, John, Toby Johnson, Katarzyna Bryc, et al. 2008. “Genes Mirror Geography Within Europe.” Nature 456 (7218): 98–101.

Olah, Christopher. 2015. Understanding LSTM Networks. https://colah.github.io/posts/2015-08-Understanding-LSTMs/.

Pearl, Judea. 2009. Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge University Press.

Pearl, Judea, Madelyn Glymour, and Nicholas P. Jewell. 2016. Causal Inference in Statistics: A Primer. Wiley.

Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. Basic Books.

Rastegarpanah, Bashir, Krishna P. Gummadi, and Mark Crovella. 2019. “Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems.” Proceedings of WSDM. http://www.cs.bu.edu/faculty/crovella/paper-archive/wsdm19-antidote-data.pdf.

Ricci, Francesco, Lior Rokach, and Bracha Shapira, eds. 2022. Recommender Systems Handbook. Springer US. https://doi.org/10.1007/978-1-0716-2197-4.

Rosenbaum, Paul R., and Donald B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70 (1): 41–55. https://doi.org/10.1093/biomet/70.1.41.

Rubin, Donald B. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.” Journal of Educational Psychology 66 (5): 688–701. https://doi.org/10.1037/h0037350.

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Sarwar, Badrul, George Karypis, Joseph Konstan, and John Riedl. 2001. “Item-Based Collaborative Filtering Recommendation Algorithms.” Proceedings of the 10th International Conference on World Wide Web, 285–95. https://doi.org/10.1145/371920.372071.

Sharma, Amit, and Emre Kiciman. 2020. DoWhy: An End-to-End Library for Causal Inference. https://arxiv.org/abs/2011.04216.

Spinelli, Larissa, and Mark Crovella. 2017. “Closed-Loop Opinion Formation.” Proceedings of the 9th International ACM Web Science Conference (WebSci). http://www.cs.bu.edu/faculty/crovella/paper-archive/netsci17-filterbubble.pdf.

Spinelli, Larissa, and Mark Crovella. 2020. “How YouTube Leads Privacy-Seeking Users Away from Reliable Information.” Proceedings of the Workshop on Fairness in User Modeling, Adaptation, and Personalization (FairUMAP). http://www.cs.bu.edu/faculty/crovella/paper-archive/youtube-fairumap20.pdf.

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Strang, Gilbert. 2019. Linear Algebra and Learning from Data. Wellesley-Cambridge Press.

Strang, Gilbert. 2023. Introduction to Linear Algebra. 6th ed. Wellesley-Cambridge Press. https://math.mit.edu/~gs/linearalgebra/.

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Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. 2018. Introduction to Data Mining. 2nd ed. Pearson. https://www-users.cse.umn.edu/~kumar001/dmbook/ch3_classification.pdf.

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Vaswani, Ashish, Noam Shazeer, Niki Parmar, et al. 2017. “Attention Is All You Need.” Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017) (Long Beach, CA, USA), 11. https://arxiv.org/abs/1706.03762.

Wang, Ruoxi, Bin Fu, Gang Fu, and Mingliang Wang. 2017. “Deep & Cross Network for Ad Click Predictions.” Proc. ADKDD, 12.

Zhang, Shuai, Yi Tay, Lina Yao, Aixin Sun, and Cheng Zhang. 2022. “Deep Learning for Recommender Systems.” In Recommender Systems Handbook, edited by Francesco Ricci, Lior Rokach, and Bracha Shapira. Springer US. https://doi.org/10.1007/978-1-0716-2197-4_5.