SC4002 - Natural Language Processing
Course Summary
This course covers a wide range of topics related to Machine Learning, revisiting many concepts, along with a significant amount of content from Neural Networks and Deep Learning (NNDL), and some material from Algorithm Design and Analysis, all applied in the context of Natural Language Processing. It’s a tough and content-heavy module. I wouldn’t recommend taking it unless you have already taken, or are taking, Machine Learning at the same time. Otherwise, you’ll struggle to keep up, especially in the second half of the course.
Workload
The workload is heavy and challenging. Without prior or concurrent knowledge of Machine Learning, you’ll find it very difficult to understand what’s going on, particularly in the latter half of the module. Revision involves a lot of material, and the final exam questions are extremely tough. You’ll need a solid grasp of the fundamental concepts to do well. The second half is especially dense, and the lecturer assumes you already know a lot about Deep Learning, although she is a very good teacher.
Projects
There is one group project that requires writing a comprehensive report on training multiple models using an NLP dataset. Start early because training and fine-tuning deep learning models can take a very long time. There are also two quizzes—one covering the first half of the course and one on the second half. The quizzes are challenging but not as difficult as the final exam. There are no makeups for missed quizzes; if you miss one, its weight will be added to the final exam.
Tips to Do Well
Begin the group project early since training deep learning models can be extremely time-consuming. Take this module only after completing Machine Learning and Neural Networks and Deep Learning (NNDL), or at least alongside them. Otherwise, you’ll find the material overwhelming unless you already have prior knowledge in these areas.
Written by CTKH