Natural Language Processing
Lecture, four hours; discussion, two hours; outside study, six hours. Requisite: course 145 or M146. Recommended requisite: course 35L. Introduction to wide range of natural language processing, tasks, algorithms for effectively solving these problems, and methods of evaluating their performance. Focus on statistical and neural-network learning algorithms that train on text corpora to automatically acquire knowledge needed to perform task. Discussion of general issues and present abstract algorithms. Assignments on theoretical foundations of linguistic phenomena and implementation of algorithms. Implemented versions of some of algorithms are provided in order to give feel for how discussed systems really work, and allow for extensions and experimentation as part of course projects. Letter grading.
Review Summary
- Clarity
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10.0 / 10
- Organization
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8.3 / 10
- Time
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5-10 hrs/week
- Overall
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10.0 / 10
Reviews
Excellent class structure and Professor Peng is an amazing teacher. There were 3 homeworks, each were pretty fair and only took a couple hours. The midterm was fair and the final was, too, but make sure to review the participation quizzes to get the "select all that apply" questions! The project was really fun and the grading was very forgiving.
The professor and TA's were very knowledgable about NLP, and overall this was a really fun class since the topics are hot and very interesting! The midterm was unrealistically hard but the teaching team took feedback very well and adjusted the grading scheme and gave us very generous bonuses for completing course evaluations. The final was much more realistic as they adjusted it accordingly. Highly recommend taking this class with Professor Peng!
The class is difficult, the midterm was far too difficult, the final was more reasonable. The professor explains everything somewhat well, but the course project is confusing. Homeworks are important since the exams are very similar in format
Displaying all 3 reviews
Course
Grading Information
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Has a group project
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Attendance required
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1 midterm
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Finals week final
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33% recommend the textbook
Previous Grades
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