Linear Algebra with Computer Science Applications (CSCI 2820)
Course Description:
In brief, this course introduces the fundamentals of linear algebra in the context of computer science applications. It includes definitions of vectors and matrices, their various operations, linear functions and equations, and least squares. It also includes the basics of floating point computation and numerical linear algebra. The list of covered topics are mentioned in details below.
In this course, the studnets will become comfortable working with the basic
tools in linear algebra and also familiar with several computer science applications throughout the semester.
List of principal topics includes:
Vectors:
Notation and terminology
Vector operations
Inner product
Linear functions, Taylor approximation, and regression model
Complex numbers and vectors
Norm, distance, and angle
Linear independence, basis, orthonormal vectors, and Gram–Schmidt algorithm
Matrices:
Notation and terminology
Matrix operations
Matrix inverses
Orthogonal matrices
QR factorization
Linear equations
Least squares:
Least squares data fitting
Multi-objective least squares
Constrained least squares
Nonlinear least squares
Textbook:
Schedule
Date | Topic | Lecture Slides | Lecture Videos | Textbook Chapters |
Jan 15 | Vectors | slide-1 | video-1 | 1.1 |
Jan 20 | Vector addition | slide-2 | video-2 | 1.2 |
Jan 22 | Scalar-vector multiplication, Inner product | slide-3 | video-3 | 1.3, 1.4 |
Jan 25 | Complexity of vector computations, Linear functions | slide-4 | video-4 | 1.5, 2.1 |
Jan 27 | Affine functions, Taylor approximation | slide-5 | video-5 | 2.2 |
Jan 29 | Regression model, Norm | slide-6 | video-6 | 2.3, 3.1 |
Feb 1 | Distance, Standard deviation | slide-7 | video-7 | 3.2, 3.3 |
Feb 3 | Angle, Complexity | slide-8 | video-8 | 3.4, 3.5 |
Feb 5 | Clustering, the k-means algorithm | slide-9 | video-9 | 4.1, 4.2, 4.3 |
Feb 8 | Examples and applications of clustering | slide-10 | video-10 | 4.4, 4.5 |
Feb 10 | Linear dependence, Basis | slide-11 | video-11 | 5.1, 5.2 |
Feb 12 | Basis, Orthonormal vectors | slide-12 | video-12 | 5.2, 5.3 |
Feb 15 | Gram-Schmidt algorithm | slide-13 | video-13 | 5.4 |
Feb 19 | Gram-Schmidt algorithm | slide-14 | video-14 | 5.4 |
Feb 22 | Matrices, Zero and identity matrices | slide-15 | video-15 | 6.1, 6.2 |
Feb 24 | Matrix addition, norm, Matrix-vector multiplication | slide-16 | video-16 | 6.3, 6.4 |
Feb 26 | Complexity, Geometric transformations, Incidence matrix | slide-17 | video-17 | 6.5, 7.1, 7.2, 7.3 |
Mar 1 | Convolution | slide-18 | video-18 | 7.4 |
Mar 3 | Convolution, Linear and affine functions | slide-19 | video-19 | 7.4, 8.1 |
Mar 5 | Affine functions, Linear function models | slide-20 | video-20 | 8.1, 8.2 |
Mar 8 | Systems of linear equations | slide-21 | video-21 | 8.3 |
Mar 10 | Null space and range space of linear maps | slide-22 | video-22 | Additional note |
Mar 12 | One-to-one and onto maps, rank of matrices | slide-23 | video-23 | Additional note |
Mar 17 | Matrix determinant | slide-24 | video-24 | Additional note |
Mar 19 | Matrix determinant and inverse | slide-25 | video-25 | Additional note |
Mar 22 | Eigenvalues and eigenvectors of matrices | slide-26 | video-26 | Additional note |
Mar 24 | Linear dynamical systems | slide-27 | video-27 | 9.1, 9.2, 9.3 |
Mar 29 | Matrix-matrix multiplication, Composition of linear functions | slide-28 | video-28 | 10.1, 10.2 |
Mar 31 | Matrix power, QR factorization, Left inverses | slide-29 | video-29 | 10.3, 10.4, 11.1 |
Apr 2 | Right inverse, Inverse | slide-30 | video-30 | 11.1, 11.2 |
Apr 5 | Solving linear equations, Pseudo-inverse | slide-31 | video-31 | 11.3, 11.5 |
Apr 7 | Least squares problem, Solution, Solving least squares problems | slide-32 | video-32 | 12.1, 12.2, 12.3, 12.4 |
Apr 9 | Least squares data fitting | slide-33 | video-33 | 13.1 |
Apr 12 | Least squares data fitting | slide-34 | video-34 | 13.1 |
Apr 14 | Validation | slide-35 | video-35 | 13.2 |
Apr 16 | Feature engineering, Classification | slide-36 | video-36 | 13.3, 14.1 |
Apr 19 | Classification, Least squares classifier | slide-37 | video-37 | 14.1, 14.2 |
Apr 23 | Multi-class classifier | slide-38 | video-38 | 14.3 |
Apr 26 | Similar matrices, Hermitian matrices | slide-39 | video-39 | Additional note |
Apr 28 | Hermitian and real symmetric matrices | slide-40 | video-40 | Additional note
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Syllabus Statements:
Classroom Behavior
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Requirements for COVID-19
As a matter of public health and safety due to the pandemic, all members of the CU Boulder community and all visitors to campus must follow university, department and building requirements, and public health orders in place to reduce the risk of spreading infectious disease. Required safety measures at CU Boulder relevant to the classroom setting include:
maintain 6-foot distancing when possible,
wear a face covering in public indoor spaces and outdoors while on campus consistent with state and county health orders,
clean local work area,
practice hand hygiene,
follow public health orders, and
if sick and you live off campus, do not come onto campus (unless instructed by a CU Healthcare professional), or if you live on-campus, please alert CU Boulder Medical Services.
Students who fail to adhere to these requirements will be asked to leave class, and students who do not leave class when asked or who refuse to comply with these requirements will be referred to Student Conduct and Conflict Resolution. For more information, see the policies on COVID-19 Health and Safety and classroom behavior and the Student Code of Conduct. If you require accommodation because a disability prevents you from fulfilling these safety measures, please see the “Accommodation for Disabilities” statement on this syllabus.
Before returning to campus, all students must complete the COVID-19 Student Health and Expectations Course. Before coming on to campus each day, all students are required to complete a Daily Health Form. In this class, you may be reminded of the responsibility to complete the Daily Health Form and given time during class to complete it. Students who have tested positive for COVID-19, have symptoms of COVID-19, or have had close contact with someone who has tested positive for or had symptoms of COVID-19 must stay home and complete the Health Questionnaire and Illness Reporting Form remotely. In this class, if you are sick or quarantined, please email the instructor about absence due to illness or quarantine.
Accommodation for Disabilities
If you qualify for accommodations because of a disability, please submit your accommodation letter from Disability Services to your faculty member in a timely manner so that your needs can be addressed. Disability Services determines accommodations based on documented disabilities in the academic environment. Information on requesting accommodations is located on the Disability Services website. Contact Disability Services at 303-492-8671 or dsinfo@colorado.edu for further assistance. If you have a temporary medical condition, see Temporary Medical Conditions on the Disability Services website.
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Honor Code
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Please know that faculty and instructors have a responsibility to inform OIEC when made aware of incidents of sexual misconduct, discrimination, harassment and/or related retaliation, to ensure that individuals impacted receive information about options for reporting and support resources.
Religious Holidays
Campus policy regarding religious observances requires that faculty make every effort to deal reasonably and fairly with all students who, because of religious obligations, have conflicts with scheduled exams, assignments or required attendance. In this class, you should notify your instructor of any conflict at least two weeks in advance.
See the campus policy regarding religious observances for full details.
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