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OVERVIEW

We’re very excited to have you in class this semester! Our aim is to make this course as inclusive, diversified, and open as possible, and we will offer an undergraduate and graduate version of the course:

  • 6.8611: Undergraduate version (CI-M component required)
  • 6.8610: Graduate version (no CI-M option)

Note: all Harvard students who wish to take the course must enroll in 6.8610.

DESCRIPTION

How can computers understand and leverage text data and human language? Natural language processing (NLP) addresses this question, and in this course students study both modern and classic approaches. We will mainly focus on statistical approaches to NLP, wherein we learn a probabilistic model based on natural language data. This course provides students with a foundation of advanced concepts and requires students to conduct a significant research project on an NLP problem of their choosing, culminating with a high-quality paper (5-8 pages). Assessment also includes (3) homework assignments and a mid-term exam. Our goal is to help challenge each student to elicit one’s best, and along the way for the course to be one of your most fun and rewarding educational experiences.

STAFF

Yoon Kim Jacob Andreas Chris Tanner Thomas Pickering Michael Maune
Yoon Kim (Instructor) Jacob Andreas (Instructor) Chris Tanner (Instructor) Thomas Pickering (WRAP) Michael Maune (WRAP)
Rebecca Thorndike-Breeze Juergen Schoenstein Kate Parsons Emily Robinson Michael Kuoch
Rebecca Thorndike-Breeze (WRAP) Juergen Schoenstein (WRAP) Kate Parsons (WRAP) Emily Robinson (WRAP) Michael Kuoch (TA)
Moulin Kaspar Sashata Sawmya Cici Xu Subha Pushpita Yung-Sung Chuang
Moulin Kaspar (TA) Sashata Sawmya (TA) Cici Xu (TA) Subha Pushpita (TA) Yung-Sung Chuang (TA)
Karissa Sanchez Aneesh Gupta Athul Jacob Belinda Li
Karissa Sanchez (TA) Aneesh Gupta (TA) Athul Jacob (TA) Belinda Li (TA)

LOGISTICS

LECTURE

  • Tuesdays and Thursdays @ 11am - 12:30pm in 32.123 (may change locations, depending on enrollment)
  • Lectures are in-person and will be recorded
  • Attendance and active participation is highly encouraged to facilitate an enriching learning environment for everyone

OFFICE HOURS

  • Mondays: 4pm - 5:30pm in 36-156
  • Tuesdays: 3pm - 4:30pm in 36-155
  • Wednesdays: 3pm - 4:30pm in 34-301
  • Fridays: 3pm - 4:30pm in 24-115

GRADING

  • Foundation: Mid-term exam: 25% (Oct 31)
  • Foundation & Application: Homework assignments: 30% (3 HWs, roughly two weeks for each)
  • Creating New Knowledge: Research Project: 45% (twelve weeks)

QUICK ACCESS

ENROLLMENT

The demand for this course content is extremely high, and we’re thrilled to see so many curious students! Over 500 students have registered for the course, so we are resource-constrained. To provide as smooth of an educational experience as possible, we strongly recommend that every enrolled student has a strong foundation in Machine Learning, and we will enforce having the sufficient pre-req courses.

PREREQUISITES

No prior NLP experience is expected or necessary, but students must have a basic foundation in probability and calculus, along with strong knowledge of Machine Learning. See the syllabus for more details, along with HW #0 (ungraded) to assess if your current knowledge is aligned with the pre-req expectations – you should be able to answer all of the questions without too much difficulty.

WHAT’S NEW?

NLP is an incredibly fast-moving field! Fun fact, ChatGPT was released the day before our final lecture of the semester last year (Fall 2022). Since then, NLP and GenerativeAI has become a household name. Toward this, we have adjusted this semester’s offering from last year’s:

  • Different homework problems
  • Some new lecture content, and we’ve rearranged the overall narrative
  • New guest lectures

NOTE: Despite the immense capabilities of large language models (LLMs), this course will be strongly rooted in providing a foundation for NLP; that is, we will cover a wide range of modelling approaches that include LLMs and more.

COURSE NAME

We acknowledge that “Quantitative Methods for NLP” may not be the most clear name. A more apt name would be “Advanced NLP”.