  While deep learning approaches represent the state-of-the-art of natural
language processing (NLP) today, classical algorithms and approaches still find
a place in NLP textbooks and courses of recent years. This paper discusses the
perspectives of conveners of two introductory NLP courses taught in Australia
and India, and examines how classical and deep learning approaches can be
balanced within the lecture plan and assessments of the courses. We also draw
parallels with the objects-first and objects-later debate in CS1 education. We
observe that teaching classical approaches adds value to student learning by
building an intuitive understanding of NLP problems, potential solutions, and
even deep learning models themselves. Despite classical approaches not being
state-of-the-art, the paper makes a case for their inclusion in NLP courses
today.
