Foundations of Speech and Language Processing

CSE 5525 · Spring 2025 · The Ohio State University

Assignment 3

Feb 20 · 0 min read

The third assignment is now ready. The instructions can be found here.

The code and dataset can be downloaded here

The gradescope autograder is not ready yet, and we will send out another announcement as soon as its available (probably over the weekend).

All the best for your assignment!

Announcements

This course constitutes an introduction to natural language processing (NLP), the goal of which is to enable computers to use human languages as input, output, or both. NLP is at the heart of many of today’s most exciting technological achievements, including machine translation, automatic conversational assistants and Internet search. The course will introduce core problems and methodologies in NLP, including machine learning, problem design, and evaluation methods.

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Lectures: WF 2:20-3:40 pm ET

Lecture Location: Jennings Hall 60

Prerequisite Knowledge

Formal

CSE 3521 or CSE 5521; CSE 5522; Stat 3460 or 3470.

Informal (please check with instructor if in any doubt)

We expect that you:

…are experienced with programming in Python – we won’t be reviewing your your code directly and we expect you to figure out the implementation side on your own; we are here to help you with understanding concepts and translating ideas into pseudocode,

…are comfortable with basic calculus, probability, and linear algebra,

…can quickly pick up machine learning and deep learning,

…can quickly pick up coding in pytorch,

…are interested in language and don’t aim to just deepen your machine learning knowledge.

Revisiting/polishing your knowledge:

There are a ton of Python resources for people with some programming experience. Check them out here. I highly recommend these slides/colab (caution: there are some typos).

Math and machine learning basics are nicely covered in the first part of the Deep Learning book. Obviously, you can use the same book to familiarize yourself with deep learning. If you learn by coding then you will find this resource helpful Practical Deep Learning for Coders by Fast.ai (3: Neural net foundations; 5: From-scratch model, 13: Backpropagation & MLP, 14: Backpropagation)

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Sachin Kumar

kumar.1145@osu.edu

Office Hours

By appointment

Teaching Assistants

Harsh Kohli

kohli.120@buckeyemail.osu.edu

Office Hours

Mondays 2:30pm - 3:30pm

Location

Online (Zoom link)

Code of Conduct

The strength of the university depends on academic and personal integrity. In this course, you must be honest and truthful, abiding by the University Academic Integrity Policy: https://oaa.osu.edu/academic-integrity-and-misconduct