Course Overview

Course Goals #

Analyze NLP techniques and apply them to text data. The course is divided into three main categories: s

  1. Preprocessing: Demonstrate how to clean and integrate text data

  2. Processing: Apply NLP algorithms on your pre-processed data to perform different tasks

  3. Post-processing: Evaluate your developed NLP models.

Solve problems with real datasets Apply practical know-how (useful for jobs, research) through significant hands-on programming assignments

Course Pre- and/or Co-Requisites #

Review our “warnings” before taking this course. You are expected to have some knowledge in Machine Learning, Linear Algebra, Optimization, Probability and Statistics. We will go over some of the Machine Learning algorithms, but we may not be able to go through them in detail. The programming language for this class is Python (Python 3.^). It is important to know at minimum how to use Numpy and its matrix operations, linear algebra, probability and statistics.

Additional Prerequisites:

  • CSE 6040
  • CS 1301

Class Text #

  • Required Readings: No required readings
  • Recommended Reading: Introduction to Natural Language Processing by Jacob Eisenstein. A draft is available on github.