CSCI 374 Resources

Handouts

• Sept. 14: Introduction to GitHub
• Sept. 14: Confidence Intervals Handout

Readings for October 10: Real-World Machine Learning Tasks

• Perez, S. September 28, 2022. "Google turns to machine learning to advance translation of text out in the real world". TechCrunch. [URL: https://techcrunch.com/2022/09/28/google-turns-to-machine-learning-to-advance-translation-of-text-out-in-the-real-world/]
• McNamee, K. July 5, 2022. "When machine learning meets surrealist art meets Reddit, you get DALL-E mini". NPR. [URL: https://www.npr.org/2022/07/05/1107126834/dall-e-mini-text-image-memes-machine-learning]
• Marr, B. September 14, 2022. "How AI And Machine Learning Will Impact The Future Of Healthcare". Forbes. [URL: https://www.forbes.com/sites/bernardmarr/2022/09/14/how-ai-and-machine-learning-will-impact-the-future-of-healthcare/?sh=6b495ab047e5]
• Ford, P. September 29, 2022. "The Race to Carbon Neutral: Fusion Energy And Machine Learning". Forbes. [URL: https://www.forbes.com/sites/forbesbusinesscouncil/2022/09/29/the-race-to-carbon-neutral-fusion-energy-and-machine-learning/?sh=609603b7266b]
• Discussion Prompts
• Reflection Assignment (Due 10/24)

Readings for November 21: Biases and Machine Learning

• Castro, Daniel. September 10, 2014. "The Rise of Data Poverty in America". [URL: http://www2.datainnovation.org/2014-data-poverty.pdf
• Nording, Linda. September 25, 2019. "A Fairer Way Forward for AI in Health Care". Nature Outlook, 593, S103-S105. [URL: https://www.nature.com/articles/d41586-019-02872-2]
• Zewe, Adam. February 21, 2022. "Can machine-learning models ovecome biased datasets?". MIT News. [URL: https://news.mit.edu/2022/machine-learning-biased-data-0221]
• Discussion Prompts
• Reflection Assignment (Due 11/30)

Recommended Textbooks

• Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. The Elements of Statistical Learning. Springer-Verlang, 2009. Website: http://web.stanford.edu/~hastie/ElemStatLearn/
• Mitchell, Tom M. Machine Learning. WCB/McGraw-Hill, Boston, MA, 1997. Website: http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html
• James, Gareth, Witten, Daniela, Hastie, Trevor, and Tibshirani, Robert. An Introduction to Statistical Learning (with Applications in R). Springer-Verlang, 2013. Website: https://www.statlearning.com
• Goodfellow, Ian, Bengio, Yoshua, and Courville, Aaron. Deep Learning. MIT Press, Cambridge, MA, 2016. Website: http://www.deeplearningbook.org/