NATURAL LANGUAGE PROCESSING

AIM

This course will cover basics of NLP and applications of deep learning in natural language processing. Prerequisite for this class is Machine Learning.

CONTENT

This course contains; Introduction,A simple NLP pipeline with scikit-learn,Word vectors,Recurrent Neural Networks,Language models,Pytorch and tensorflow,Text classification, text summarization, question answering,Exam Week study,Machine translation,Transformers,Lightweight AI,NLP systems in production,Project presentations,Project presentations.

1. 1

   Implement advanced neural network architectures using tensorflow or pytorch.

   Taught by: Project Based Learning Model · Assessed by: Homework

2. 2

   Complete a full NLP project involving advanced concepts in machine learning

   Taught by: Question - Answer Technique, Project Based Learning Model · Assessed by: Oral Exam, Project Task

3. 3

   Describe various NLP algorithms such as those used for text classification and text generation

   Taught by: Problem Solving Method, Self Study Method, Simulation Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Oral Exam, Quiz

WEEKLY PLAN

1. WEEK 1

   Introduction

2. WEEK 2

   A simple NLP pipeline with scikit-learn

3. WEEK 3

   Word vectors

4. WEEK 4

   Recurrent Neural Networks

5. WEEK 5

   Language models

6. WEEK 6

   Pytorch and tensorflow

7. WEEK 7

   Text classification, text summarization, question answering

8. WEEK 8

   Exam Week study

9. WEEK 9

   Machine translation

10. WEEK 10

    Transformers

11. WEEK 11

    Lightweight AI

12. WEEK 12

    NLP systems in production

13. WEEK 13

    Project presentations

14. WEEK 14

    Project presentations

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Speech and Language Processing, Jurafsky and Martin, 3rd edition draft at https://web.stanford.edu/~jurafsky/slp3/
• Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper at http://www.nltk.org/book/

TEACHING STAFF

• Prof.Dr. Selim AKYOKUŞCOORDINATOR
• Prof.Dr. Selim AKYOKUŞ