 Course description
 Matrices and linear transformations, systems of linear equations,
determinants, linear vector spaces, eigenvalues and eigenvectors,
orthogonal matrices, diagonalization, least squares, and singular
value decomposition.
Topics include:
 Solving systems of linear equations
 Vector space, basis and dimension
 Least squares solutions
 Orthogonalization by GramSchmidt
 Properties of determinant
 Eigenvalues and eigenvectors
 Symmetric matrices and positive definite matrices
Applications
 Piazza
 We will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates and myself. Rather than emailing questions to me, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com.
Find our class page at: https://piazza.com/uci/winter2017/ics6n/home
 Lab Assignments
 Lab
1 Week 3 (Jan 24, Tue)
 Lab
2 Week 5 (Feb 7, Tue)
 Lab
3 Week 7 (Feb 21, Tue)
 Lab
4 Week 10 (Mar 16, Thur)
 Homework Assignments
 HW1  due Jan 17 (Tue) before class (from the
textbook)
 1.3: 1,2,3,4,5,6
 1.1: 1,3,11,13
 HW2  due Jan 24 (Tue) before class
 1.2: 1,2,3,4,8,10,12,14,19,20
 HW3  due Jan 31 (Tue) before class
 1.3: 14,15
 1.4: 14,12,13,22,23
 2.1: 12
 HW4  due Feb 14 (Tue) before class
 2.1: 2728, 33
 2.2: 7,10,3132
 HW5  due Feb 21 (Tue) before class
 3.1: 12,14
 3.2: 810, 2123, 2728, 3136
 4.1: 1,11,13
4.2: 5,6,24
 HW6  due Mar 2 (Thur) before class
 4.6: 14,1718,31
 5.1: 1314,2122, 25, 27
 5.2: 12 1517, 18
 5.3: 34, 9,15,17,19,2122
 HW7  due Mar 9 (Thur) before class
 5.6: 1,2,15
 6.1: 18, 13,1518,20,23,25
 HW8  due Mar 16 (Thur) before class
 6.2: 10, 12, 2324, 29
 6.3: 3,9,17,18
 6.4: 3,7,9
 6.5: 1,5,17
 7.1: 23,24
 7.3: 9
 Lecture notes

Introduction, Vectors

Vectors,
Matrices

Systems
of linear equations, Gaussian elimination

Row
reduction, Echelon form

Vector
Equations, Linear Combinations, Span

Matrix
equation, Linear independence

Matrix
algebra, Transpose

Matrix inverse, Elementary Matrices

Determinent,
Properties of matrix determinant, Cofactor formula

Vector
space, Subspace, Null space, Column Space, Spanning set, Basis,
Dimension, Rank

Eigenvalues,
Eigenvectors, Matrix diagonalization, Matrix power, Discrete
Dynamical Systems

Distance,
Orthogonality, Least Squares, Projections, GramSchmidt Process

Symmetric
Matrices, Orthogonal Diagonlization, Quardratic Forms, Constrained
Optimization, Principle Component Analysis
 Textbook
 Linear Algebra and its Applications, 4th edition, by David
Lay
 Polices
 Grading criteria:
 Homework (25%)
 Lab assignments (15%)
 Two quizzes (30%)
 Final (30%)
 Homework: You may discuss each assignment with others, but are
required to code and write up each assignment independently.
 Late homework policy: If you get a note from the Student's Office
(personal problems) or infirmary (medical problems) requesting a
postponement, it will be honored. Otherwise, late homework will not
be accepted.
 Lab: There are four lab assignments due on days and times as given
above. All of the labs will require some programming in
Matlab. There are many online tutorials available to you if you
decide you need some help in addition to what is provided in the
assignment.

 Lab assignments will consist of performing some Matlab
instructions as well as written work. The results of all the
questions should be copied into a pdf file. You will hand in both
the pdf file and a file containing all commands you run for each
lab.
 Matlab is installed in many machines in the lab. In addition to
scheduled lab time for this class, you can drop in on any of the
following labs to have access to a computer with Matlab. If the room
is being used by an instructor as part of their schedule lab time,
you can ask them if you can use one of the computers during their
class.
 ICS 365. Lab hours are posted here. Matlab
is installed in only some of the
machines. Ask the lab attendant
if you need help finding
one.
 MSTB 210 Lab hours are posted here.
 SBSG 240 Lab hours are posted here.
 Final Exam: No makeup for missed final will be entertained unless
there is a documented evidence of medical emergency. Makeup exams
carry less weightage than the one posted on this webpage. If you
miss the final exam for any other reason, you will receive a zero on
the exam.
 In addition to the above work load and point distribution, 1% is
given to the students who submit the final course evaluation. Though
the instruction staff cannot see the evaluation until the grades the
submitted, they can see the names of the students who have submitted
the evaluation, in order for them to give this additional
point.
 Above point distribution may be changed at the discretion of the
instructor without prior notice or discussion, typically in favor of
students.
 Obtaining
Assistance
 Please use Piazza for questions related to homework, lab, exams
and teaching material. For private questions, send email with your
questions to all the teaching staff for quicker response. Subject
line should start with the string `ICS 6N`. The message should
contain your name and student number.
 You are responsible for anything communicated in class by the
instructor, including class announcements. If you have to miss
lecture for any reason, please ask one of your classmates to fill
you in on what you missed. You can use the message board to ask
general questions that your classmates can answer.
 Lab time is an important time to get questions answered about
homework problems and labs. Some lab time will be spent walking you
through features of Matlab that you will need for your lab
assignments. Lab time is the best time for troubleshooting issues
that come up with Matlab or lab assignments.
 Questions about grading should be asked to the person who graded
your paper during his/her office hours or by appointment.
 Academic
Honesty
The Bren School of ICS and the University have already established
an academic honesty policy. Read it.
Violators of academic honesty policies are subject to the penalties
described in the Bren School of ICS policy. They are also subject to
an immediate course grade of F, and you will not be allowed to drop
the course to avoid the grade. Also be aware that a single
documented case of academic dishonesty may preclude you from
switching into computing majors, registering for computing minors,
joining the ICS Honors Program, and graduating from a computing
major with honors.
 Guidelines to avoid plagiarism
Do not look at another person's homework. Instead you should prefer
to discuss the problem in plain English. This helps you to
communicate clearly, practice technical jargon as it applies to your
problem, and to identify how your solution exhibits behavior
different from what you expect.
Do not write down the solution in your notes. It is perfectly fine
(and encouraged) to collaborate on work. Working in a group is a
rewarding experience, and definitely a necessary skill in any
professional career. The collaboration can include drawing diagrams
and perhaps solving the problem on a whiteboard. However, you should
avoid writing the solution in your notes. It is very useful to
rethink the problem and go through the details and logic when you
solve it again on your own.
 We expect
that
You can monitor each other and enforce these rules among
yourselves. Making sure that others follow these guidelines will
help to ensure that they don't pass off your work as their own.
Your work honestly represents your efforts. The entire purpose of
obtaining an education is so that you can accumulate a body of
skills and experience that will help you later on. If you do not
perform the work yourself, then you have cheated yourself out of the
education. Employers in our field can (and do) screen applicants for
skills and knowledge. You will perform poorly (and discredit UCI) if
you do not practice now by doing your own work.