3. 강의목표
Machine learning becomes a popular tool to predict and understand real world datasets. This class aims to provide a basic mathematical/statistical tools required to understand various machine learning algorithms. A wide range of basic mathematical concepts including but not limited to linear algebra, analytic geometry, matrix decomposition, vector calculus, probability and distribution, continuous optimization will be provided.
4. 강의선수/수강필수사항
A basic understanding of probability/statistics is required.
Recommended courses: MATH203, MATH230, IMEN261
5. 성적평가
- midterm exam (30%)
- final exam (30%)
- assignments, quiz, etc (40%)
6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
|
Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Cambridge University Press 2020
|
|
|
0000
|
|
7. 참고문헌 및 자료
CS229: Machine Learning, Stanford University
CSC411: Introduction to Machine Learning, University of Toronto
MIT 6.036: Introduction to Machine Learning
8. 강의진도계획
Week1: Introduction and Motivation
- Week2-3: Linear Algebra
- Week3-4: Analytic Geometry
- Week5-6: Matrix Decompositions
- Week6-7: Vector Calculus
- Week8: Mid-term
- Week9: Probability and Distribution
- Week9-10: Continuous Optimization
- Week11: When Models Meet Data
- Week12: Linear Regression
- Week13: Dimensionality Reduction with Principal Component Analysis
- Week14: Density Estimation with Gaussian Mixture Models
- Week15: Classification with Support Vector Machines
- Week16: Final
9. 수업운영
Offline lectures are provided throughout the semester. 4~5 assignments including programming with mid- and final-exams will be used to evaluate the performance.
10. 학습법 소개 및 기타사항
교과목 이수구분
- 2024학번까지: 전공선택
- 2025학번부터: 전공선택필수
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