Here are lecture notes from the distance learning classes to help
people catch up who may have experienced technical difficulties during
Attendance is mandatory and will be taken starting
with the second week of class.
Don't forget to check
graded discussions, pending homework assignments, quizzes and the
schedule of Zoom lectures.
This is the study day after the last day of class and
before the final exam.
[23-Sep-2020] Quiz 1
Quiz 1 will be held in class over zoom on September 23. This exam
will be closed book and closed notes. Please
make sure your web cameras are working and have your UNR student
ID at hand for identity verification. Note that it will not be
possible to take this exam without a working web camera.
Check your system is working ahead of time and after that don't
change any configuration settings until after the exam.
You will also need your own pencil and paper.
As stated in the course schedule, Quiz 1 will cover Chapter 1 through
Chapter 4 from our text. In addition to the first four homework
assignments, please review the following topics and tasks:
Be able to add vectors and multiply them by a scaler.
Be able to form inner product of two vectors.
Know the exact definition of a linear function.
Vector form of the first order Taylor approximation.
Define and compute Euclidean norms.
Define and compute rms(x), avg(x) and std(x).
State the Chebyshev inequality and use it (page 47 in the textbook).
State the Cauchy-Schwarz inequality and the
condition under which it is exact (pages 56-57 in the text).
Be able to algebraically derive the triangle inequality
starting with the Cauchy-Schwarz inequality (see lecture
notes from September 9).
Define correlation coefficient and use it to compare
vectors (see Slide 3.25 of the lecture notes or
page 61 in the textbook).
State the objective function J used in k-means clustering
(see Slide 4.4 of the lecture notes).
Explain the steps in the k-means algorithm (Algorithm 4.1
on page 75 of the textbook).
Again, please also be prepared to work problems similar to those
appearing in the first four homework assignments.
My solutions to these homework
assignments have been provided below. Note that no
homework problems from Chapter 4 have been assigned.
Over the summer I made a video showing how to download and run
the JupyterLab notebook for the example code in VMLS Julia
Language Companion on k-means clustering.
[30-Aug-2020] Installing JupyterLab
JupyterLab provides a notebook interface that makes using Julia
easier to use and also allows one to experiment with the code
samples from the Julia Companion. Here is a video demonstration
how to install the JupyterLab notebook interface.
[24-Aug-2020] First Day of Class
We will meet over Zoom at 5:30pm.
the meeting link.
[23-Aug-2020] Installing Julia
We will be using the Julia during this course.
This software is open source and available for Windows,
Macintosh and Linux. Please try to install Julia
your home computer and let me know how it goes.
My suggestion is to download the installer from
the official project
site for Julia and then follow your mouse. Here is
a video demonstration.
Once people have installed Julia and verified it is working,
I'll further describe how to install the JupyterLab notebook interface.
[19-Aug-2020] Zoom for Students
Information on Zoom for Students is
If you sign into Zoom ahead of time using your UNR student
account at the UNR Zoom website, you
can enter the online class lectures directly and bypass
the waiting room.
[07-Aug-2020] Tentative Course Map
This course will cover the first 12 chapters of the primary
text by Boyd and Vandenberghe and then switch
to the secondary text by Matthews to cover chapters 4 and 6.
I have created a tentative course
map describing how everything should fit together.
Note the graded homework assignments listed in the
course map will likely be changed. Please refer to the homework
section and calendar below along with our
page for an updated list of problems and deadlines.
I will be giving online interactive lectures through the
Zoom Video Conferencing system
If possible, please install and
test this software before the first day of class. Note that
the university has sponsored Zoom accounts for every student.
Accounts may be activated by visiting
You do not need to pay Zoom any money
to use this software on your home computer.
is that study rooms may be reserved in Mathewson-IGT Knowledge
Center and equipment
out from the @One Digital Media
and Technology Center by students who need a suitable location
to attend lectures delivered over Zoom.
This course will be delivered through the
UNR WebCampus a customized version of the
learning management platform. According to
documentation Canvas supports access from Windows, MacOS
and Linux using current and first previous major releases of
the Chrome, Firefox, Edge and Safari browsers. If you
are having trouble accessing WebCampus from home or
on campus, please contact
the UNR OIT Helpdesk.
Julia is a free open-source software designed at MIT for performing
matrix and vector computations similar to Matlab.
This language is quickly becoming popular in science, technology,
engineering and mathematics because it is easy to use and generally
performs faster than Matlab.
Click and install versions can be downloaded for Windows, macOS and
Linux from the
official Julia language website. If you try to download it
over summer and encounter difficulties, please let me know.
[01-Aug-2020] Free Online Textbook
Due to a special arrangement with Cambridge University Press
the book for our course is
available for free download.
A physical copy of the printed book costs
about $40 dollars from the Wolfshop UNR Bookstore as well as online.
According to the back cover,
This groundbreaking textbook combines straightforward explanations with a
wealth of practical examples to offer an innovative approach to teaching
linear algebra. Requiring no prior knowledge of the subject, it covers
the aspects of linear algebra--vectors, matrices, and least squares--that
are needed for engineering applications, discussing examples across
data science, machine learning and artificial intelligence, signal and
image processing, tomography, navigation, control, and finance. The
numerous practical exercises throughout allow students to test their
understanding and translate their knowledge into solving real-world
problems, with lecture slides, additional computational exercises in
Julia and MATLAB, and data sets accompanying the book online. Suitable
for both one-semester and one-quarter courses, as well as self-study,
this self-contained text provides beginning students with the foundation
they need to progress to more advanced study.
In my opinion the readability coupled with the existence of a companion
text on using Julia
to easily perform vector and matrix calculations makes this
book particularly well suited for online learning. My hope is you will agree.
[31-Jul-2020] Alternative Remote
This course was originally scheduled to be delivered in-person,
but has moved to entirely online due to social distancing and
We will be using a combination of Zoom,
WebCampus and other
Internet resources which will be announced later.
Luckily, this course will not include the additional $34
per credit online fee; however,
please make sure you have a computer, suitable web camera and
the Internet connection needed for online learning.
Exams and quizzes will be interpreted according to the following
Grade Minimum Percentage
A 90 %
B 80 %
C 70 %
D 60 %
The instructor reserves the right to give plus or minus grades
and higher grades than shown on the scale if he believes they
Aug 24-Aug 28 Module 1 Vectors
Aug 31-Sep 04 Module 2 Linear Functions
*** Labor Day Sep 07
Sep 07-Sep 11 Module 3 Norm and Distance
Sep 14-Sep 18 Module 4 Clustering
Sep 21-Sep 25 Module 5 Linear Independence
*** Quiz 1 covering modules 1-4 Sep 23
Sep 28-Oct 02 Module 6 Matrices
Sep 05-Oct 09 Module 7 Matrix Examples
Oct 12-Oct 16 Module 9 Linear Equations
Oct 19-Oct 23 Module 10 Matrix Multiplication
*** Quiz 2 covering modules 5-7 Oct 21
Oct 26-Oct 30 Module 11 Matrix Inverses
*** Nevada Day Oct 30
Nov 02-Nov 06 Module 12 Least Squares
Nov 09-Nov 13
*** Quiz 3 covering modules 10-11 Nov 9
*** Veteran's Day Nov 11
Nov 16-Nov 20 Module 13 Determinants
Nov 23-Nov 27 Module 14 Eigenvectors Eigenvalues
*** Thanksgiving Nov 26
*** Family Day Nov 27
Nov 30-Dec 04 Module 14 continued
Dec 07-Dec 08
*** Prep Day Dec 9
*** Final exam Dec 11 from 4:50 to 6:50pm
Lectures and classroom activities will be held online through Zoom at the
scheduled meeting time listed in MyNevada for this course. Please check
the canvas page for the Meeting ID and Join URL under the Zoom tab. To
promote an open communication through this interactive environment,
video attendance will be mandatory and count as participation in your
final grade. If you wish to set up an appointment for office hours
please send me a message through
ask through chat after one of the online lectures.
Students must have an approved university excuse to be eligible for a
make-up exam. If you know that you will miss a scheduled exam please
let me know as soon as possible. Homework may be turned in late--with a
possible deduction of points depending on the circumstances--as long as
I have not already graded the assignment. When attending a Zoom lecture
for the course, it's always better to be late than never.
Students are encouraged to work in groups and consult resources outside
of the required textbook when doing the homework for this class. Please
cite any sources you used to complete your work including Wikipedia, other
books, online discussion groups as well as personal communications. Exams
and quizzes, unless otherwise noted, will be closed book, closed notes
and must reflect your own independent work. Please consult the section
on academic conduct below for additional information.
A web camera will be required for this course in order to comply with
university requirements for identity verification. Bring your student
ID to all online quizzes and Zoom lectures as if attending class on
campus. At the beginning of each class please send a quick hello through
chat and a quick goodbye at the end. This will indicate to me that you
are ready and also count towards your attendance and participation score.
This course is designed to comply with but not satisfy the UNR Core
Objective 10 requirement on diversity and equity. More information about
the core curriculum may be found in the UNR Catalog
Statement on COVID-19 Training Policies
Students must complete and follow all guidelines as stated in the Student
COVID-19 Training modules, or any other trainings or directives provided
by the University.
Statement on COVID-19 Face Coverings
In response to COVID-19, and in alignment with State of Nevada Governor
Executive Orders, Roadmap to Recovery for Nevada plans, Nevada System
of Higher Education directives, the University of Nevada President
directives, and local, state, and national health official guidelines
face coverings are required at all times while on campus, except when
alone in a private office. This includes the classroom, laboratory,
studio, creative space, or any type of in-person instructional activity,
and public spaces.
A "face covering" is defined as a covering that fully covers a person's
nose and mouth, including without limitation, cloth face mask, surgical
mask, towels, scarves, and bandanas (State of Nevada Emergency Directive 024).
Students that cannot wear a face covering due to a medical condition or
disability, or who are unable to remove a mask without assistance may seek
an accommodation through the Disability Resource Center.
Statement on COVID-19 Social Distancing
Face coverings are not a substitute for social distancing. Students shall
observe current social distancing guidelines where possible in accordance
with the Phase we are in while in the classroom, laboratory, studio,
creative space (hereafter referred to as instructional space) setting and
in public spaces. Students should avoid congregating around instructional
space entrances before or after class sessions. If the instructional
space has designated entrance and exit doors students are required to
use them. Students should exit the instructional space immediately after
the end of instruction to help ensure social distancing and allow for
the persons attending the next scheduled class session to enter.
Statement on COVID-19 Disinfecting Your Learning Space
Disinfecting supplies are provided for you to disinfect your learning
space. You may also use your own disinfecting supplies.
Contact with Someone Testing Positive for COVID-19
Students must conduct daily health checks in accordance with CDC
guidelines. Students testing positive for COVID-19, exhibiting
COVID-19 symptoms or who have been in direct contact with someone
testing positive for COVID-19 will not be allowed to attend in-person
instructional activities and must leave the venue immediately. Students
should contact the Student Health Center or their health care provider to
receive care and who can provide the latest direction on quarantine and
self-isolation. Contact your instructor immediately to make instructional
and learning arrangements.
Your student fees cover usage of the University Math Center, University
Tutoring Center, and University Writing and Speaking Center. These
centers support your classroom learning; it is your responsibility to
take advantage of their services. Keep in mind that seeking help outside
of class is the sign of a responsible and successful student.
Equal Opportunity Statement
The Mathematics Department is committed to equal opportunity in education
for all students, including those with documented physical disabilities
or documented learning disabilities. University policy states that it is
the responsibility of students with documented disabilities to speak
with the Disability Resource
Center during the first week of each semester to discuss appropriate
accommodations to ensure equity in grading, classroom experiences and
For assistance with accessibility, or to report an issue,
please use the
Help Form. The form is set up to automatically route your request
to the appropriate office that can best assist you.
Statement on Audio and Video Recording
Surreptitious or covert video-taping of class or unauthorized audio
recording of class is prohibited by law and by Board of Regents
policy. This class may be videotaped or audio recorded only with the
written permission of the instructor. In order to accommodate students
with disabilities, some students may be given permission to record class
lectures and discussions. Therefore, students should understand that
their comments during class may be recorded.
Bring your student identification to all exams.
Work independently on all exams and quizzes.
Behaviors inappropriate to test taking may disturb other
students and will be considered cheating.
Don't send electronic messages, talk or pass notes with other
students during a quiz or exam.
Homework may be discussed freely.
When taking a quiz or exam over Zoom or in the classroom
don't read notes or books unless explicitly permitted.
Sanctions for violations are specified in the
University Academic Standards Policy.
If you are unclear as to what constitutes cheating,
please consult with me.
The final exam will be held on Friday, December 11
from 4:50 to 6:50pm through alternative remote. Please make
sure you have a web camera available for the final exam.
Fri Aug 7 18:32:01 PDT 2020