Resources

• Google Drive. Course lectures, reading lists, etc., are in a Google Drive folder which will be shared with all students. You will also use Google Drive for submitting your quizzes and project proposal, progress report and final report.

• Slack. Students may reach out to the instructor and TAs for questions and comments over the private Slack channel, #fall22-csci-699-30041, on the USC Viterbi School of Engineering Classes workspace.

• Mailing List. Announcements for the entire class will be made via the mailing list the enrolled students have been added to, in addition to the course website.

Required Readings

Required readings and supplementary materials will be available on the course website for each class in PDF form.

Background Readings

Students might optionally read:

• Eisenstein. “Natural Language Processing.” This textbook contains an overview of machine learning approaches for NLP.
• Hardt and Recht. “Patterns, Predictions, And Actions” This textbook contains an overview of machine learning.
• Barocas, Hardt, and Narayanan. “Fairness and Machine Learning: Limitations and Opportunities” This books contains an overview of fairness in machine learning.
• D’Ignazio and Klein. “Data Feminism”. This textbook provides an ethical foundation for many sub-disciplines of data science.
• Goldberg. “Neural Network Methods for Natural Language Processing.” This textbook provides a deep learning perspective towards NLP.