Traditionally, Computational Linguistics focuses on computational methods and algorithms applied to the various levels of the famous "Linguistic Skyscraper". From bottom to top, imagine a sky-high building where each level of linguistic analysis cascades down (or up, depending how you see it) in relative force and intermingled/lax boundaries:

-Phonology/Phonetics
-Morphology
-Syntax
-Semantics
-Pragmatics.

That is how computational syntax and computational semantics were first conceived.

In recent years, Computational Linguistics has been expanded to include applications to corpora (bodies of oral or written language), learning from big data (such as those on the web and social media), and statistically parsing natural language. Each of these applications have been extremely popular with NLP and CL researchers due to the current demands and prevailing need of making sense of natural language data used in everyday 'social networking' venues (and their business/marketing counterparts).

What follows are proposed "topical" syllabi (i.e. syllabi with a focus on topics and 'units' taught rather than specific "calendars") for each of these areas along with selected relevant teaching materials:

1. Corpus Linguistics
2. Machine Learning
3. Statistical NLP