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- Kai-Wei Chang
- COM SCI 263
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- Uses Slides
- Useful Textbooks
- Tough Tests
- Participation Matters
- Has Group Projects
Grade distributions are collected using data from the UCLA Registrar’s Office.
Grade distributions are collected using data from the UCLA Registrar’s Office.
Grade distributions are collected using data from the UCLA Registrar’s Office.
Grade distributions are collected using data from the UCLA Registrar’s Office.
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This class is an introduction to NLP and covers tasks such as part-of-speech tagging, word representation, syntactic parsing, semantic parsing, co-reference resolution, machine translation and more. The models and algorithms used for these tasks are a mixture of classical ones (e.g Hidden Markov Models) and modern ones (e.g Transformer neural nets), where the class focuses more on the latter.
Generally, I am very happy with Prof Chang's delivery of this material. The lectures are well-prepared and interactive and are updated regularly to include new concepts, interesting papers, etc. I especially like the quality of the lecture slides, which are almost good enough to learn from on entirely their own.
One issue I had with the class is that it is fairly work-intensive. Here is the list of assignments in the class:
-Weekly quizzes (5 in total)
-1 midterm group project
-1 paper group presentation
-1 final group project
-1 final exam
-Various peer reviews
While there are quite a few, I did like the hands-on nature of these assignments. We could implement a range of different approaches for each project and even had the opportunity to peer-review other students' work. I found the latter especially useful as it gives you a better way to compare and learn than only receiving a grade.
Overall I can really recommend this class to someone interested in NLP. Its material is current and the instructors genuinely want to help you learn about the field.
This class is an introduction to NLP and covers tasks such as part-of-speech tagging, word representation, syntactic parsing, semantic parsing, co-reference resolution, machine translation and more. The models and algorithms used for these tasks are a mixture of classical ones (e.g Hidden Markov Models) and modern ones (e.g Transformer neural nets), where the class focuses more on the latter.
Generally, I am very happy with Prof Chang's delivery of this material. The lectures are well-prepared and interactive and are updated regularly to include new concepts, interesting papers, etc. I especially like the quality of the lecture slides, which are almost good enough to learn from on entirely their own.
One issue I had with the class is that it is fairly work-intensive. Here is the list of assignments in the class:
-Weekly quizzes (5 in total)
-1 midterm group project
-1 paper group presentation
-1 final group project
-1 final exam
-Various peer reviews
While there are quite a few, I did like the hands-on nature of these assignments. We could implement a range of different approaches for each project and even had the opportunity to peer-review other students' work. I found the latter especially useful as it gives you a better way to compare and learn than only receiving a grade.
Overall I can really recommend this class to someone interested in NLP. Its material is current and the instructors genuinely want to help you learn about the field.
Based on 1 User
TOP TAGS
- Uses Slides (1)
- Useful Textbooks (1)
- Tough Tests (1)
- Participation Matters (1)
- Has Group Projects (1)