Course description

In this course, we will study contemporary machine learning methods for understanding and generation of human language. If you have taken machine learning and know how to implement, train, and deploy a classifier -- and now you want to understand how to bridge the gap between that and contemporary language models that can answer questions and hold a conversation, this course is for you. Through a series of homeworks, you will learn how to implement and train neural models to process language, from word-level embeddings to Transformer language models, including implementation, pre-training, fine-tuning and other modes of deployment of such models for handling different aspects of processing human language.

Pre-requisite: COMP 5500 / COMP 4600 Machine Learning or equivalent (with permission of instructor).

Class format

Each class will be divided into

• Lecture
• Practicum/Lab

There will be a 10-minute break after the lecture.

During the Practicum/Lab segment of the class, we will focus on technical (coding) skills and provide homework guidance. You will be expected to work on your homework during this time, so please bring your laptop.

Course materials

Class recordings will be available on Echo.

Class-related discussions and announcements will be conducted on Discord.

COVID Safety

If you have any flu-like symptoms, or if you or somebody close to you have tested positive for COVID, please do not attend the class in person. If you are able to, please join the class meeting remotely. Please notify your instructor if you will need to join the class remotely.

Staff

	Name	Contact	Office	Office hours
Instructor	Anna Rumshisky	arumshisky@gmail.com	Dandeneau 318	TBA
TA	Namrata Shivagunde	namrata_shivagunde@student.uml.edu	Dandeneau 415	TBA
TA	Vijeta Deshpande	vijeta_deshpande@student.uml.edu	Dandeneau 415	TBA
TA	Sherin Muckatira	sherinbojappa_muckatira@student.uml.edu	Dandeneau 415	TBA

Remote learning

Class recordings will be available on Echo (you need to log in with your University logon):

Echo recordings

Cheat sheets:

• Machine learning cheat sheet
• Review: Math preliminaries
• Python cheat sheet
• Numpy cheat shet
• Review: Mathematics for deep learning
• Github for beginners
• Github help

Grading

Homeworks	20%
In-Class Quizzes	10%
Midterm Exam	25%
Final Exam	30%
Research Paper Presentations	10%
Attendance and Participation	5%

Homeworks

• We will have 6-7 homeworks.
• Homeworks are due at midnight on the day they are due.
• Homeworks will be posted on the course website and linked from the course schedule
• Homeworks must be submitted via Blackboard.

We will discuss homework solutions in class the following week, and will ask you to self-grade each homework after the solution is discussed

Exams and Quizzes

Exams and quizzes will be closed book and will consist of (1) theoretical questions based the material discussed in class and (2) question based on homework assignments. You may be asked to reproduce a part of your solution to homework questions and provide explanations.

Research Paper Presentations

• Each student will be required to present a research paper assigned as readings for the class.

Late Policy:

• Homeworks will be accepted up to 2 (two) days after the original due date.
• Homeworks submitted up to 1 full day late will be graded at a 10% reduction.
• Homeworks submitted up to 2 full days late will be graded at a 20% reduction.
• After 2 days, Homeworks will not be accepted.

Collaboration Policy:

• Homeworks must be done individually.

Violating the collaboration policy by copying other people's work, as well as any other instance of cheating, including copying solutions from existing sources or submitting AI-generated code, carries the following penalties: (1) First violation leads to getting zero credit for the submitted assignment (2) Second violation leads to failing the course.