COMP 790.139: Natural Language Processing (Fall 2017)
Instructor: Mohit Bansal
TA: Yixin Nie
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This course will be based on the artificial intelligence and machine learning field of natural language processing (NLP, or computational linguistics), and its important multimodal connections to computer vision and robotics. First we will cover traditional topics of NLP such as tagging, parsing, coreference resolution, sentiment analysis, summarization, question-answering, and translation. Then, we will cover more recent topics of multimodal, grounded, and embodied semantics (i.e., language with vision and speech, for robotics), language generation and dialogue, and interpretable deep learning.
Topics (tentative, based on time)
Since this is a graduate research-level class, some machine learning and coding experience is expected (see references below). Previous courses in probability and linear algebra are also highly recommended.
Grading (tentative)
Grading will consist of:
Students are allowed 3 free late days for assignments over the semester. After that, late assignments will be accepted with a 10-20% reduction in value per day late.
Collaboration Policy
Homeworks and paper summaries have to be written and submitted individually. Projects are encouraged to be done in pairs (but individual projects are fine too, e.g., if it relates to your current research), with clearly outlined contributions from each team member.
Reference Books
Schedule (tentative)
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The professor reserves the right to make changes to the syllabus, including project due dates. These changes will be announced as early as possible.