COM SCI 162
Natural Language Processing
Description: 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.
Units: 4.0
Units: 4.0
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Most Helpful Review
Winter 2023 - Great introduction to NLP and its modern day applications! I learned so much from this course alone. I liked the way the professor explained the material, how it was super engaging and super relevant to how NLP is actually used in the real world. It has even made me consider a career in the NLP/ML field. The core topic of this course IMO is language models. You start from the very basics (n-grams), and the professor explains step-by-step how language models have evolved over the past few years, culminating at the various transformer models (BERT, GPT). Grading was very fair and lenient. There were plenty of extra credit opportunities on the projects, and she gave everyone 5 % boost on their midterm and final grades for completing a course survey. The exams themselves were very fair; as long as you understand the lectures, you should be all set. I also do want to mention the course project, since it allows you to actually have hands-on experience with how researchers are using NLP in the real world. It is a fairly involved group project, but you learn a ton and it could pay off in the future if you choose to pursue something NLP related. That being said, the project takes a LOT longer than one might think, as training the transformer models on the GPUs took forever and the VM environment in which we trained the models kept crashing.
Winter 2023 - Great introduction to NLP and its modern day applications! I learned so much from this course alone. I liked the way the professor explained the material, how it was super engaging and super relevant to how NLP is actually used in the real world. It has even made me consider a career in the NLP/ML field. The core topic of this course IMO is language models. You start from the very basics (n-grams), and the professor explains step-by-step how language models have evolved over the past few years, culminating at the various transformer models (BERT, GPT). Grading was very fair and lenient. There were plenty of extra credit opportunities on the projects, and she gave everyone 5 % boost on their midterm and final grades for completing a course survey. The exams themselves were very fair; as long as you understand the lectures, you should be all set. I also do want to mention the course project, since it allows you to actually have hands-on experience with how researchers are using NLP in the real world. It is a fairly involved group project, but you learn a ton and it could pay off in the future if you choose to pursue something NLP related. That being said, the project takes a LOT longer than one might think, as training the transformer models on the GPUs took forever and the VM environment in which we trained the models kept crashing.
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