CSCI 374 Resources

Handouts

• Sept. 10: Introduction to GitHub
• Sept. 12: Confidence Intervals Handout
• Nov. 12: Naive Bayes Exercise and Data and Probabilities

Readings for October 17: Real-World Machine Learning Tasks

• Davis, Nicola. September 24, 2019. "AI equal with human experts in medical diagnosis, study finds". The Guardian. [URL: https://www.theguardian.com/technology/2019/sep/24/ai-equal-with-human-experts-in-medical-diagnosis-study-finds]
  Original journal article for those interested (not required reading): https://www.thelancet.com/journals/landig/article/PIIS2589-7500(19)30123-2/fulltext)
• Falk, Dan. March 17, 2019. "AI Algorithms are Now Shockingly Good at Doing Science". Wired. [URL: https://www.wired.com/story/ai-algorithms-are-now-shockingly-good-at-doing-science/]
• Niiler, Eric. March 25, 2019. "Can AI Be a Fair Judge in Court? Estonia Thinks So". Wired. [URL: https://www.wired.com/story/can-ai-be-fair-judge-court-estonia-thinks-so/]
• Lewis, Neil & Burnell, Max. September 30, 2019. "Look and feel good: How tech could save the fashion industry". CNN Business. [URL: https://www.cnn.com/2019/09/27/business/technology-fashion-sustainability/index.html]

Readings for December 4: 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]
• Vincent, James. January 23, 2018. "Artificial Intelligence is Going to Supercharge Surveillance". The Verge. [URL: https://www.theverge.com/2018/1/23/16907238/artificial-intelligence-surveillance-cameras-security

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: http://faculty.marshall.usc.edu/gareth-james/ISL/
• Goodfellow, Ian, Bengio, Yoshua, and Courville, Aaron. Deep Learning. MIT Press, Cambridge, MA, 2016. Website: http://www.deeplearningbook.org/

Related Podcasts:

NB: these podcasts are posted to inspire thought and do not necessarily represent the views or opinions of the professor. Each video is copyright the speakers and are the original material of the presenters.

• Talking Machines: Human Conversations about Machine Learning [link]

Related Videos

NB: these videos are posted to inspire thought and do not necessarily represent the views or opinions of the professor. Each video is copyright TED and are the original material of the presenters.

TED Talks on Machine Learning: A collection of thought provoking talks by machine learning researchers and practitioners at TED conferences.

Jeremy Howard: "The Wonderful and Terrifying Implications of Computers that can Learn" from TEDxBrussels (Dec 2014) [Machine Learning, Applications, Society]

Tom Gruber: "How AI can Enhance Our Memory, Work, and Social Lives" from TED2019 (April 2017) [Machine Learning, Applications]

Kenneth Cukier: "Big data is better data" from TEDSalon Berlin 2014 (June 2014) [Data, Society]