CS7.501 Advanced NLP

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 absolute newcomers to the field. While specific prerequisites are not strictly enforced, prior experience is highly encouraged. Tutorials will be held to equip students with necessary mathematical and engineering background.

• Prerequisites: NLP Basics, Machine Learning, Deep Learning

Previous Editions - Monsoon '25

Introduction to NLP - Spring '26

Instructors

Manish Shrivastava

Aparajitha Allamraju

Teaching Assistants

Yajat

Druhan

Ishaan

Maitreya

Manas

Monish

Vivek

Logistics ↑ Top

• Class Timing: Mondays & Thursdays, 11:40-13:05
• Location: H205
• Course Workload: This course will consist of approximately 26 lectures, 4 assignments, 2 exams and 1 project.

All course related annoucements will be made on Moodle.

Teaching Assistant Office Hours

* Appointments to be requested via email the previous day by 23:59.

Course Schedule ↑ Top

Due Dates ↑ Top

Important dates for the course:

Grading and Weightage ↑ Top

Your final grade in this course will be determined by the following components:

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 or 4 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.

Seminar ↑ Top

An optional seminar shall be available for up to 16 students as a substitute for an exam. Approved students will need to thoroughly understand and present a topic to the class and engage in a QnA with the audience. Students will be evaluated on topic understanding, coverage, presentation and ability to answer questions. The seminar will involve combining multiple papers to present the topic (akin to a survey paper), instead of presenting just one paper.

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 can be seen as an opportunity to acknowledge significant contributions and foster academic collaboration.

Other Resources ↑ Top