This cource gives comprehensive lectures and projects about NLP. (All materials have been kept strictly confidential.)

Chapter 0. Preparation

03/03 Lecture 1. Course Introduction

03/03 Lecture 2. Programming Language Basics for Project

Chapter 1. Background of AI

03/08 Lecture 3. Overview of AI

03/10-03/15 Lecture 4. Problem Solving

03/17 Lecture 5. Model Representation

03/22-03/31 Lecture 6. Optimization & Generalization

04/05 Lecture 7. Evaluation

04/07-04/14 Lecture 8. Probabilistic Modeling

Basics

Bayesian Network

Markov Random Field

04/19-04/21 Lecture 9. Logic Modeling

Background

Predicate Calculus

Inductive Logit Programming

Mid-term exam -> Replaced to Lab 2 Project

Chapter 2. Statistical Relational Learning

04/26-04/28 Lecture 10. Overview of SRL

Hyperplane-based Modeling - Neural Networks

Why SRL?

05/03-05/12 Lecture 11. Integration of Logic and Probability

Bayesian Logic Programming

Markov Logic Network

Logic Modeling: Basics

Logic Modeling: Formal Grammar

Probabilistic Context Free Grammar

Chapter 3. Natural Language Processing

05/17-05/26 Lecture 12. Overview of Natural Language Processing

Linguistics Essentials + More Terms

NLP Problems and Their Relations

N-gram

05/31 Lecture 13. Language Model

06/02 Lecture 14. Machine Translation

06/07 Lecture 15. Question Answering

06/09 Lecture 16. Conversational System

06/14 Final-Term Exam

• Date: June 14, 2022
• Time: 10:30 a.m ~ 11:45 a.m.
• Venue: EECS C2 B101
• Exam: Whole lectures. No coding assignment contents.

Projects

Project contents introduce basic practical knowledge different to the contents of the lectures. To cover the simple knowledge and programming issues, we will have 6 lab times in this semester (in our class time, no additional lab times are assigned.)

• Lab 0, 1, 2, 3 : will be uploaded every 2 weeks
• Lab 4, 5 : will be uploaded every 3 weeks

CHECK the key date of each assignment: (To be uploaded; Q&A Session; Deadline)

• To be uploaded: the slide and video about the introduction of Lab N will be upload on time (not real-time). You can start from this date.
• Q&A Session: interactive Q&A Session will be held on time (on Zoom meeting; real-time).
• Deadline: you should submit your assignment before the deadline

Lab 0 - Environmental Settings (slide, video) [0%]

• Schedule: (03.10; 03.15; No deadline)
• Links: (kaggle, github) (In this assignment, no links)
• Materials:
  • How to Use Colab (Machine Setting (Google Colab)/ guide for getting started)
  • How to Use GIST SW Center GPU Machine (user manual kor / eng) (Official video and document from SW Center) (GPU Server Specifications: 1 GPU; 3 CPU; 12GB RAM; 100GB HDD
  • How to submit your code and results (Kaggle / Github Repository)

Lab 1 - Representation of Symbolic Data (no slide, video) [10%]

• Schedule: (03.24; 03.29; 04.06 23:59 KST)
• Links: (kaggle pb1, pb2 / github)
• Materials: Please see the materials on Github classroom

Lab 2 - NLP data preparation (slide, video) [10%; mid-term project]

• Schedule: (04.07; 04.12; 04.20 23:59 KST / 04.20 14:59 UTC)
• Links: (kaggle, github)
• Materials: Please see the contents on kaggle and the slide

Lab 3 - Problem Formulation (slide, video) [10%]

• Schedule: (04.21; 04.26; 05.04 23:59 KST / 05.04 14:59 UTC)
• Links: (kaggle pb1, pb2 / github)
• Materials: Please see the contents on Kaggle and the Slide

Lab 4 - Encoder-Decoder Implementation (slide, video - part1, 2) [10%]

• Schedule: (05.06; 05.12; 05.25 23:59 KST / 05.25 14:59 UTC)
• Links: (kaggle, github)
• Materials: Please see the contents on kaggle and the slide

Lab 5 - Transformer Implementation (slide, video) [10%]

• Schedule: (05.26; 05.31; 06.15 23:59 KST / 06.15 14:59 UTC)
• Links: (kaggle, github)
• Materials: Please see the contents on Kaggle and the Silde