Modern Natural Language Processing

3 March 2020

Overview

Natural language processing has gone through a series of innovations in the last few years. Deep learning achieved state-of-the-art results on many tasks, and lowered the entry barrier for newcomers. This course will focus on modern word and meaning representation, and compare them to classic linguistic approaches. We will talk about statistical learning and language modelling and demonstrate some of these methods' weak points.

Target Audience

The target audience of this course is data-scientists and software developers who are proficient in other fields of machine learning and would like to get a crash course on natural language processing applications.

Prerequisites

Learning Outcomes

Graduates of this course will be able to:

Syllabus

Week
01

Introduction to Natural Language Processing

  • Intro to Linguistics
  • Phonemes / Morphemes / Language Trees
  • Syntactic / Semantic Representation
  • Natural Language Tasks

Week
02

Statistical Modeling of Language

  • The Bag-of-Words Model
  • Hypothesis Testing on Words and Sentences
  • Bayesian Model of a Language
  • Document / Sentence Representation

Week
03

Syntax and Semnatics

  • Part-of-Speech Tagging
  • Intro to Parse Trees
  • Word2vec and extensions

Week
04

Deep Learning for Natural Language Processing

  • Recurrent Neural Networks
  • Transformers and Intro to BERT


Registration

Course requires a minimum number of students to open.

Contact Us

Please don't hesitate contacting us with any questions or requests you have regarding this course.

Phone

052-5896673

Contact Form