CS7.501 Advanced NLP
Monsoon 2025
Course Overview ↑ Top
Designed for those seeking an advanced understanding, this course explores in-depth topics in Natural Language Processing (NLP). It assumes foundational knowledge and may not suitable for newcomers to the field. While specific prerequisites are not strictly enforced, prior experience is highly encouraged.
- Prerequisites: NLP Basics, Machine Learning, Deep Learning
Instructor
Teaching Assistants
Logistics ↑ Top
- Class Timing: Mondays & Thursdays, 11:40-13:05
- Location: H-104
- Course Workload: This course will consist of approximately 28 lectures, 3 assignments, 2 quizes and 1 project.
All course related annoucements will be made on Moodle.
Teaching Assistant Office Hours
| TA Name | Office Hours |
|---|---|
| Aparajitha | Monday: 13:30 - 14:30 (MT NLP Lab) |
| Saumitra | Thursday: 10:30 - 11:30 (MT NLP Lab) |
| Maitreya | Friday: 9:00 - 10:00 (Appointments must be requested via email by Thursday, 9:00 PM) |
| Ketaki | Monday: 14:00 - 15:00 |
| Vivek | Monday: 10:00 - 11:00 |
| Raveesh | Thursday: 14:00 - 15:00 |
Course Schedule ↑ Top
Grading and Weightage ↑ Top
Your final grade in this course will be determined by the following components:
| Component | Weightage |
|---|---|
| Assignments * 3 | 10 + 10 + 10 (30%) |
| Quiz * 2 | 10 + 10 (20%) |
| Project | 50% |
| Total | 100% |
Due Dates ↑ Top
Important dates for the course:
| Component | Due Date |
|---|---|
| Team Details Submission | 7-Aug |
| Project Proposals (Interim) | 10-Aug |
| Proposal (Interim) Rejection by TAs | 12-Aug |
| Project Finalization, Mentor Assignment | 15-Aug |
| A1 Release | 17-Aug |
| Proposal (Final) | 26-Aug |
| Proposal Grades | 5-Sep |
| A1 Due | 10-Sep |
| A2 Release | 12-Sep |
| A1 Grades | 21-Sep |
| Project Mid Submission | 2-Oct |
| A2 Due | 10-Oct |
| Project Mid Grades | 12-Oct |
| A3 Release | 12-Oct |
| A2 Grades | 3-Nov |
| Final Project Due | 7-Nov |
| A3 Due | 10-Nov |
| Q1 Release | 12-Nov |
| Q1 Due | 14-Nov |
| Q2 Release | 17-Nov |
| Q2 Due | 19-Nov |
| A3 Grades | 23-Nov |
| Final Grade Assignment | 5-Dec |
Course Projects ↑ Top
The course project is a significant component of this course (50%), providing an opportunity to apply the concepts and techniques learned to a real-world NLP problem. Students will work in teams of 3 to propose, develop, and present a project. Projects are expected to be at an advanced level. Submission to conferences/workshops is highly encouraged. Read more on research opportunities.
- Interim Project Proposal: A brief document outlining your project idea in a couple of paragraphs.
- Project Proposal: A document outlining your project idea, backgroud, goals, dataset, and methodology in 2-3 pages.
- Mid Submission: Mid checkin on your progress.
- Final Submission: A comprehensive paper/report detailing your project, experiments, results, and conclusions along with code/other resources and presentation or demonstration of your project.
We encourage students to think creatively and explore areas that genuinely interest them.
| Component | Weightage |
|---|---|
| Project Outline | 5% |
| Project Mid | 15% |
| Project Final | 30% |
| Total | 50% |
Project Areas
- Efficient/Low-Resource Methods
- Ethics, Fairness, Bias
- Generation/Language Modeling
- Interpretability/Explainability
- Multilingual/Cross-lingual
- Multimodal
- NLP Applications
- Evaluation/Benchmarking
- Retrieval & Extraction
- Document Understanding
- Graph + LLMs
- Entertainment + LLM
Assignments and Project Policies ↑ Top
Late Submission Policy
Assignment and project deadlines are strictly enforced. Late submissions will not be allowed and no extensions will be provided.
Collaboration Policy
Collaboration on assignments is encouraged for discussion of concepts and general approaches, but all submitted answers must be your individual work. For projects, group collaboration is expected, and the contribution of each member should be clearly documented. Any specific collaboration guidelines for individual assignments or projects will be provided with the assignment description.
Consultation and Research Opportunities
Students are highly encouraged to consult with the instructor, teaching assistants and mentors during office hours or by appointment for guidance on assignments, projects, or course topics. If a project developed in this course shows significant potential for a research paper, students may, at their discretion, invite a TA, a mentor or the instructor whose advice substantially benefited the work to be a co-author. This is not a requirement, but an opportunity to acknowledge significant contributions and foster academic collaboration.
Other Resources ↑ Top
- Deep Learning by Ian Goodfellow (Book)
- Understanding Deep Learning by Simon Prince (Book)
- Mathematics for Machine Learning by Deisenroth, Faisal, Ong (Book)
- PyTorch Tutorials
- 🤗 Transformers