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Natural Language Processing

Course Summary

Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing.


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    Course Syllabus

    Topics covered include: 1. Language modeling.
    2. Hidden Markov models, and tagging problems.
    3. Probabilistic context-free grammars, and the parsing problem.
    4. Statistical approaches to machine translation.
    5. Log-linear models, and their application to NLP problems.
    6. Unsupervised and semi-supervised learning in NLP.

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    Recommended Background

    A basic knowledge of probability (e.g., you should be familiar with random variables, independence assumptions, etc.), a basic knowledge of algorithms, and a basic knowledge of calculus (e.g., how to differentiate simple functions). 

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    Course Format

    The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments.

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    Suggested Reading

    The course will be largely self-contained, with comprehensive lecture notes posted together with the lectures.


Course Fee:
Free

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

This course is listed under Development & Implementations and Data & Information Management Community

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