How should natural languages be understood and analyzed? In this course, we will examine some of the modern computational approaches, mainly using deep learning, to understanding, processing and using natural languages. Unlike conventional approaches to language understanding, we will focus on how to represent and manipulate linguistic symbols in a continuous space.
The course is mainly intended for master- and doctorate-level students in computer science and data science. The number of seats is limited, and the priority is given to the students enrolled in the master’s programme at the Center for Data Science and those in the Ph.D. programme of the Department of Computer Science, Courant Institute of Mathematical Sciences.
Lecture: 5.10pm - 7.00pm on Monday at Warren Weaver Hall 202
Laboratory: 5.10pm - 6.00pm on Wednesday at BOBS LL139
Instructor: Kyunghyun Cho
Grading (tentative): Prerequisite Knowledge Test (5%) + Lab Assignments (30%) + Final Project (50%) + In-Class Exam (15%)
Course Site: NYUClasses will be used extensively for the following purposes
Book: There will be no single textbook. A reading list for each lecture will be provided separately, and a student is expected to read them before the lecture. However, the following book (draft) is highly recommended during the course:
A student is expected to be familiar with the following topics:
A student is encouraged to try the following languages/frameworks in advance:
A student is expected to have taken the following courses before taking this course:
This course is complementary to
First of all, it is mandatory to attend the first eight lab sessions. Missing any of these sessions will result in a lower grade/score.
There will be three lab assignments during these eight lab sessions:
For each lab assignment, a student is expected to hand in a short report outlining the model, its implementation and experimental results (up to 3 pages long) and present the working code to the TA in charge during the lab session. Note that office hours are not meant for assisting students on these assignments.
In this course, a student is expected to conduct a research project related to the topics presented during the lectures. The topic of each research project is to be agreed upon with the lecturer and teaching assistants based on the topic proposal submitted by a student. The deadline for the topic proposal is 26 October, and the proposal should consist of up to 3 pages of the description of the topic, method and experimental procedures. Once the proposal has been submitted, the student will receive a confirmation and feedback by email from the lecturer and/or teaching assistants in two weeks.
The final report is due on the last lecture (14 December.) The final report should include the description of the task, models, experiments and conclusion and be up to 6 pages long (a more specific instruction on the format will be announced later.) The deadline will not be extended.
Some of the candidate topics include, but are definitely not limited to,
Students are encouraged to find recent literatures on one of these topics and prepare to discuss it with the lecturer and/or teaching assistants, in order to narrow down a specific topic. Students are encouraged and expected to use the lab sessions to ask the questions on practical issues implementing these models and running experiments. At each lab session, one of the teaching assistants or the lecturer will be present.