Highlights

• Course Schedule
• A Simple Instruction of Using Rivanna

1. Course Information

• Instructor: Yangfeng Ji (Office hour: TBA; Location: Rice 510)
• Semester: Fall 2024
• Location: Olsson Hall 005
• Time: TuTh 12:30 PM - 1:45 PM
• TA:
  • Caroline Gihlstorf (Office hour: TBA)
  • Elizabeth Palmieri (Office hour: TBA)
  • Nibir Chandra Mandal (Office hour: TBA)

From the instructor: I lost track of email requests about class enrollment. If you are still interested in this course, please send me an email (yangfeng at virginia) with only “Course Enrollment Request” in the subject line and a short description of the related courses that you have taken so far.

1.1 Additional Information

• Schedule
• Campuswire for online discussion. By the time of our first class, students registered for this course should all receive an invitation from Piazza. Please let the instructor know if you haven’t gotten one.
• Homework submission template for homework assignments
• All course lectures will be recorded and uploaded to Collab automatically. Please do not distribute the videos outside the class. Please refer to this page for more information about the recording policy.

2. Course Description

Natural language processing (NLP) seeks to provide computers with the ability to process and understand human language intelligently. Examples of NLP techniques include (i) automatically translating from one natural language to another, (ii) analyzing documents to answer related questions or make related predictions, and (iii) generating texts to help story writing or build conversational agents. This course, consisting of one fundamental part and one advanced part, will give an overview of modern NLP techniques.

2.1 Topics

This course will mainly focus on applying machine learning (particularly, deep learning) techniques to natural language processing. NLP topics covered by this course

1. Text classification and its applications
2. Word embeddings
3. Language modeling
4. Sequence-to-sequence models and machine translation
5. Large language models and text generation

Other advanced topics, such as explainable NLP and NLP in social science.

For detail information, please refer to the course schedule.

2.2 Prerequisites

• Proficiency in Python
  This course requires some programming in both homeworks and the final project. The preference of programming language for this course is Python (with some additional packages like Scipy, Sklearn, and PyTorch).
• Calculus and Linear Algebra
  Multivariable derivatives, matrix/vector notations and operations; singular value decomposition, etc.
• Probability and Statistics
  Mean and variance, multinomial distribution, conditional dependence, maximum likelihood estimation, Bayes theorem, etc.
• Foundations of Machine Learning
  Logistic regression, cross validation, optimization with gradient descent, bias and variance decomposition, etc.

2.3 Textbooks

• [JE] Eisenstein, Natural Language Processing, 2018

3. Assignments and Final Project

• Homework (60%):
  • There will be four homeworks, one for each main topics covered in this course
  • Each homework assignment is worth 18%.
• Project (40%):
  There is only one course project and the credit breaks down to four parts. Students should team up for this project, each group should have 2 - 3 students.
  • Project proposal: 10%
  • Mid-term report: 10%
  • Final project report: 10%
  • Final project presentation: 10%
• In both homework and the final project, other than using the machine learning libraries including Sklearn, PyTorch, Tensorflow, students need to implemented the rest of the proposed model by themselves. Copying code from any resources (e.g., Github, Bitbucket, and Gitlab) is prohibited and will be considered as plagiarism.

3.1 Collaboration policy

For homework assignments

• Students should be fully responsible for the answers in their own submissions.
• Students are allowed to discuss homework with their classmates. If you discuss with your classmates, please disclose their names in your submission. Directly copying answers from others is definitely considered as plagiarism.
• It is not prohibited to use generative AI tools (e.g., ChatGPT) for reference, but an acknowledgment is mandatory if students do use these tools.
• Instructors reserve the possibility of requesting further clarification during grading (via Emails or Canvas discussion), and the grading will reflect students’ understanding of their own answers.

For the final project, replace the word “student(s)” with “group(s)”.

Last updated on 07/21/2024