About

Class Description

Over the last two and a half decades, computational linguistics has been revolutionized as a result of three closely related developments: increases in computing power, the advent of large linguistic datasets, and paradigm shifts toward probabilistic modeling and deep learning. At the same time, similar theoretical developments in cognitive science have led to a view of major aspects of human cognition as instances of rational statistical inference. These developments have set the stage for renewed interest in computational approaches to how humans use language. Correspondingly, this course covers some of the most exciting developments in computational psycholinguistics over the past decade. The course spans human language comprehension, production, and acquisition, and covers key phenomena spanning phonetics, phonology, morphology, syntax, semantics, and pragmatics. Students will learn technical tools including probabilistic models, formal grammars, neural networks, and decision theory, and how theory, computational modeling, and data can be combined to advance our fundamental understanding of human language acquisition and use.

Intended Audience

Undergraduate or graduate students in Brain & Cognitive Sciences, Linguistics, Electrical Engineering & Computer Science, and any of a number of related disciplines. The undergraduate section is 9.19, the graduate section is 9.190. Postdocs and faculty are also welcome to participate!

The course prerequisites are:

  1. One semester of Python programming (fulfillable by 6.00/6.0001+6.0002, for example), plus
  2. Either:
    • one semester of probability/statistics/machine learning (fulfilled by, for example, 6.041B or 9.40), or
    • one semester of introductory linguistics (fulfilled by 24.900 or equivalent).

If you think you have the requisite background but have not taken the specific courses just mentioned, please talk to the instructor to work out whether you should take this course or do other prerequisites first.

We will be doing Python programming in this course, and also using programs that must be run from the Unix/Linux/OS X command line. If you have less than one semester of Python programming experience and/or would like to strengthen your Python programming background, I have listed some resources recommended by others here.

If you are unsure about whether this class is a good fit for you given your background and interests, please consult the class FAQ; if you are still unsure, please contact Roger.