3. 강의목표
The main objective of this course is to provide students with practical knowledge and hands-on experience in critical topics and tools related to mathematical analysis and optimization, specifically focusing on their application in developing AI algorithms. The course will initially explore the computational aspects of essential topics in matrix and vector analysis, real and functional analysis, convex optimization, and constrained optimization. Additionally, the course will delve into the practical applications of these topics, such as Gaussian processes, latent models, and statistical data analysis. Upon completion of the course, students will possess a comprehensive understanding of these concepts and their practical implementation in algorithm development
The learning outcomes of this course are as follows:
- Demonstrate a systematic knowledge of fundamental objects and tools in mathematical analysis and optimization, with the ability to contextualize this knowledge in AI through algorithmic development.
- Develop and critically evaluate objects and tools in analysis and optimization relevant to AI problems.
- Implement appropriate algorithms to solve problems that arise in AI.
4. 강의선수/수강필수사항
MATH230 (확률및통계), EECE233 (신호및시스템)
5. 성적평가
Midterm exam (30%), Coursework (40%), Final project (30%)
8. 강의진도계획
W01 Course introduction & topics in matrix and vector analysis, and probability
W02 Topics in matrix and vector analysis, and probability - 2
W03 Topics in matrix and vector analysis, and probability - 3
W04 Topics in analysis and optimization - 1
W05 Topics in analysis and optimization - 2
W06 Topics in analysis and optimization - 3
W07 Topics in constrained optimization
W08 Mid-term exam
W09 Topics in vector analysis and statistical data analysis - 1
W10 Topics in vector analysis and statistical data analysis - 2
W11 Topics in vector analysis and statistical data analysis - 3
W12 Advanced statistical data analysis techniques- 1
W13 Advanced statistical data analysis techniques- 2
W14 Analysis in machine learning - 1
W15 Analysis in machine learning - 2
W16 Final 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