@inproceedings{kennington-2021-natural,
    title = "Natural Language Processing for Computer Scientists and Data Scientists at a Large State University",
    author = "Kennington, Casey",
    editor = "Jurgens, David  and
      Kolhatkar, Varada  and
      Li, Lucy  and
      Mieskes, Margot  and
      Pedersen, Ted",
    booktitle = "Proceedings of the Fifth Workshop on Teaching NLP",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.teachingnlp-1.21/",
    doi = "10.18653/v1/2021.teachingnlp-1.21",
    pages = "115--124",
    abstract = "The field of Natural Language Processing (NLP) changes rapidly, requiring course offerings to adjust with those changes, and NLP is not just for computer scientists; it{'}s a field that should be accessible to anyone who has a sufficient background. In this paper, I explain how students with Computer Science and Data Science backgrounds can be well-prepared for an upper-division NLP course at a large state university. The course covers probability and information theory, elementary linguistics, machine and deep learning, with an attempt to balance theoretical ideas and concepts with practical applications. I explain the course objectives, topics and assignments, reflect on adjustments to the course over the last four years, as well as feedback from students."
}