3. 강의목표
This class aims at learning fundamentals of statistical inference algorithms. The topics covered in this class include:
1. Review of random process
2. MMSE estimator and Kalman filter
3. Graphical model and Viterbi, forward-and-backward, and message passing algorithms
4. Large-scale optimization for sparse structured signals
5. Fundamental of deep learning - a large scale optimization point-of-view
4. 강의선수/수강필수사항
1. Probability and random process
2. Introduction to digital communications
3. Information theory
(If you do not take any of these three courses, please do not register, this is an advanced course for upper level graduate students)
5. 성적평가
1. Exam: 40%
2. Homework: 20%
3. Participation: 10%
4. Term Project: 30%
6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
|
Random processs for engineers
|
Bruce Hajek
|
Cambridge University Press
|
0000
|
|
|
Pattern Recognition and Machine Learning
|
Christopher M. Bishop
|
Springer
|
0000
|
|
7. 참고문헌 및 자료
1. Information theory, inference, and learning algorithms (David J. C. MacKay)
8. 강의진도계획
1. Probability review
2. Random process review
3. Bayesian filters: MMSE, Kalman filter
4. Markov chain
5. MAP, MMSE, and EM algorithm in Markov process
6. Forward-backward algorithm, Viterbi, and EM algorithms in Hidden Markov process
7. Graphical model (factor graphs)
8. Message-passing algorithm
9. Variational Inference
10. Principal Component Analysis
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