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강연자 정교민
소속 서울대 전기공학부
date 2014-10-16
이 강연에서는 최근 음악, 영화 추천 등 다양한 Recommendation System의 기본 아이디어인 Matrix Completion 문제와, 이를 해결하기 위해 Singular Value Decomposition을 통한 차원 축소 및 내재 공간 학습이 어떤 원리로 이루어 지는지 설명합니다. 그리고 PageRank 등 검색 알고리즘, 머신 러닝의 최적화 문제 등에서의 수학의 중요성을 설명하고자 합니다.

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첨부 '1'
  1. The classification of fusion categories and operator algebras

  2. Green’s function for initial-boundary value problem

  3. Mechanization of proof: from 4-Color theorem to compiler verification

  4. On the distributions of partition ranks and cranks

  5. Q-curvature in conformal geometry

  6. Zeros of the derivatives of the Riemann zeta function

  7. Geometry, algebra and computation in moduli theory

  8. Gromov-Witten-Floer theory and Lagrangian intersections in symplectic topology

  9. High dimensional nonlinear dynamics

  10. What is model theory?

  11. Essential dimension of simple algebras

  12. Restriction theorems for real and complex curves

  13. 20Oct
    by 김수현
    in 수학강연회

    Recommendation system and matrix completion: SVD and its applications (학부생을 위한 강연)

  14. Deformation spaces of Kleinian groups and beyond

  15. Idempotents and topologies

  16. Recent progress on the Brascamp-Lieb inequality and applications

  17. Existence of positive solutions for φ-Laplacian systems

  18. Riemann-Hilbert correspondence for irregular holonomic D-modules

  19. Normal form reduction for unconditional well-posedness of canonical dispersive equations

  20. Random conformal geometry of Coulomb gas formalism

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