The application of neural network methods - under the name Deep Learning - has led to breakthroughs in a wide range of fields, including in building language technologies (e.g. for search, translation, text input prediction). This course will provide a hands-on introduction to the use of deep learning methods for processing natural language. Methods to be covered include static word embeddings, feed-forward networks for text, recurrent neural networks, transformers, pre-training and transfer learning, with applications including sentiment analysis, translation, generation, and testing Linguistic theory.
| Days | Time | Location |
|---|---|---|
| Monday and Wednesday | 10:25 - 11:40 AM | Hylan 307 |
| Role | Name | Office | Office Hours |
|---|---|---|---|
| Instructor | C.M. Downey | Lattimore 507 | Wednesdays 2-4pm |
While relevant readings are posted in the schedule below, the following are very good general resources. Names that are used to refer to these works are included in brackets.
Students will complete 8 homeworks, comprised of both written and (Python) programming assignments. Unless noted otherwise on the schedule, homeworks will be released on Wednesdays, and due at 11pm on the following Wednesday. All homework will be submitted via Blackboard.
All deadlines and meeting times for this class are in "Eastern Time". Please note: on Sunday November 3, this will change from Eastern Daylight Time (EDT/UTC-4) to Eastern Standard Time (EST/UTC-5).
All work should be submitted by 11:00pm the day it is due. Work that is received late will incur the following penalties:
Extensions (without penalty) may be offered if they are requested within a reasonable amount of time (relative to the reason for the extension) before the work is due. Please don't hesitate to ask for an extension if you need one.
The latter portion of the course will focus on examples of Deep Learning being applied to Linguistics and Linguistic Theory. Students will pick a scholarly paper featuring such an application and present the work during class, including leading a discussion. Depending on course enrollment, this may be completed inidividually or as a small group.
Students will not be penalized because of important civic, ethnic, family or religious obligations, or university service. You will have a chance, whenever feasible, to make up within a reasonable time any assignment that is missed for these reasons. Absences for these reasons will count as excused for the sake of the participation grade. But it is your job to inform me of any expected missed work in advance, as soon as possible.
All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy. More information is available here. Please note: The use of Generative AI to produce any part of the written or programming assignments is not allowed. Due to the topicality of the course, I will make an exception if you implement and train the model yourself (i.e. no use of pre-trained weights or API calls to pre-existing models), and turn in the implementation with the assignment you used in on. For the sake of your time, I do not recommend this option.