NTU Course
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Linear Algebra

Offered in 112-2

  • Serial Number

    15832

  • Course Number

    IM1014

  • Course Identifier

    705 10700

  • No Class

  • 3 Credits

  • Preallocated

    DEPARTMENT OF INFORMATION MANAGEMENT

      Preallocated

    • DEPARTMENT OF INFORMATION MANAGEMENT

  • CHIA-YEN LEE
  • Schedule Location
    • Tue 6
      BLDG. 1 COLLEGE OF MANAG.1F Room101 (管一101)
    • Fri 2, 3, 4
      BLDG. 1 COLLEGE OF MANAG.1F Room103 (管一103)

  • Type 2

  • 80 Student Quota

    NTU 80

  • No Specialization Program

  • Chinese
  • NTU COOL
  • Core Capabilities and Curriculum Planning
  • Notes
    The course is conducted in English but uses Chinese textbook。
  • Limits on Course Adding / Dropping

    • Restriction: within this department (including students taking minor and dual degree program)

  • NTU Enrollment Status

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  • Course Description
    This course will investigate the theory, algorithms, and applications related to linear algebra. Course topics include linear equations, matrices, linear transformations, linear dependence, determinants, solution spaces, inner product spaces, eigenvalues, eigenvectors, etc.
  • Course Objective
    The goal of this course is to introduce the mathematical concept of linear systems, provide students with the foundation of linear algebra, familiarize students with theoretical derivation, and cultivate students' ability to use linear algebra to solve related problems. At the same time, it provides a good academic foundation for contemporary artificial intelligence, machine learning, and data science.
  • Course Requirement
    This course has midterm exams, final exams, and program assignments. Exams are Closed book exams.
  • Expected weekly study hours after class
  • Office Hour
  • Designated Reading
    Elementary Linear Algebra with Applications, 9th Ed. by Bernard Kolman and David Hill.; ISBN: 978-1-29202-365-6
  • References
    Essence of Linear Algebra https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab&hl=zh-cn
  • Grading
    10%

    Recitation Attendance

    20%

    Program Assignments

    35%

    Midterm Exam

    35%

    Final Exam

  • Adjustment methods for students
    Adjustment MethodDescription
    Teaching methods

    Assisted by video

  • Course Schedule
    2/20,2/23Week 1Ch 1. Linear Equations and Matrices
    2/27,3/01Week 2Ch 2. Solving Linear System
    3/05,3/08Week 3Ch 3 Determinant
    3/12,3/15Week 4Ch 3 Determinant
    3/19,3/22Week 5Ch 4 Real Vector Spaces
    3/26,3/29Week 6Ch 4 Real Vector Spaces
    4/02,4/05Week 7Ch 5 Inner Product Spaces
    4/09,4/12Week 8Midterm Exam
    4/16,4/19Week 9Ch 5 Inner Product Spaces
    4/23,4/26Week 10Ch 6 Linear Transformations and Matrices
    4/30,5/03Week 11Ch 6 Linear Transformations and Matrices
    5/07,5/10Week 12Ch 7 Eigenvalues and Eigenvectors
    5/14,5/17Week 13Ch 7 Eigenvalues and Eigenvectors
    5/21,5/24Week 14Ch 8 Applications- Singular Value Decomposition (SVD)
    5/28,5/31Week 15Applications of Linear Algebra in Machine Learning and Data Science
    6/04, 6/07Week 16Final Exam