3. 강의목표
- Course Overview: This course covers the basic and applications of natural language processing.
- Educational Objectives: This course provides students with opportunities to explore the fundamental concepts of natural language processing (NLP) technologies and gain hands-on project experience. After completing the course, students are expected to have both a theoretical and practical foundation in NLP.
5. 성적평가
| 중간고사 |
기말고사 |
출석 |
과제 |
프로젝트 |
발표/토론 |
실험/실습 |
퀴즈 |
기타 |
계 |
|
|
|
|
|
|
|
|
|
|
| 비고 |
(*Tentative) Exams(60%), Quiz(5%), Assignment(20%), Project(15%)
|
6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
|
- Lecture slides
|
|
|
0000
|
|
7. 참고문헌 및 자료
- Speech and Language Processing (3rd ed. draft)
8. 강의진도계획
(*Tentative Schedule)
Week 1: Introduction
Week 2: Regular Expressions, Tokenization, Edit Distance
Week 3: N-gram Language Model
Week 4: Naive Bayes, Text Classification, and Sentiment
Week 5: Logistic Regression
Week 6: Vector Semantics and Embeddings
Week 7: Neural Networks
Week 8: Midterm Exam
Week 9: RNNs and LSTMs
Week 10: Transformers
Week 11: Large Language Models
Week 12: Masked Language Models
Week 13: Model Alignment, Prompting, and In-Context Learning
Week 14: NLP Applications
Week 15: Project Presentation
Week 16: Final Exam
9. 수업운영
- Lecture Type: Offline
- Teaching Methods: Theoretical lectures, coding practice, team projects.
11. 장애학생에 대한 학습지원 사항
- 수강 관련: 문자 통역(청각), 교과목 보조(발달), 노트필기(전 유형) 등
- 시험 관련: 시험시간 연장(필요시 전 유형), 시험지 확대 복사(시각) 등
- 기타 추가 요청사항 발생 시 장애학생지원센터(279-2434)로 요청