Office Hours:

The instructor and TAs will hold weekly office hours via zoom. Please find the office hours and Zoom links on Canvas (Austin, TX time).

Textbook information:

Notes are linked from edX. All of these materials are freely available. The title of the electronic book is “Advanced Linear Algebra: Foundations to Frontiers (ALAFF)” .

A lot of our effort went into writing notes that are structured to fit with the edX platform. They   are available at the course start but may be adjusted. There are hyperlinks in the unit on edX that take you to the material in the document.

When can I watch the lectures, read notes, and work on the homework activities or Matlab?

After the release of the course, you can watch the lectures, read notes, or work on assignments at any time you choose. Find below a schedule of due dates. Homework is due at 23:59 CST each   Sunday. Please make sure you submit them by this time.

Grading:

Grades will be assigned based on homework, proofs and programming exercises, and three exams, two of which are open note an untimed.

In addition to the homework on the edX platform, which is graded automatically for completion, there will be canvas submitted proofs and program assignments. The honor code for all exams will be enforced.

Exams – 39%,

Midterm 1, timed, closed note, theory based, open 2/15, 12:01AM – 2/18, 11:59PM – 12%

Midterm 2, untimed, open note, programming based, open 3/24, 12:01AM – 3/31, 11:59PM – 12%

Final exam, untimed, open note, programming based, open 3/21, 12:01AM – 5/1, 11:59PM – 15%

EdX Homework Progress, two lowest dropped – 22%

Submitted Proofs/Programs Homework, two lowest dropped – 39%

Exams:

There are three exams: two midterms, and a comprehensive final. The midterms cover all topics   up to that point in the course and the final covers the entire course.  Why do we have the exams?  They may provide evidence of what and how much you are learning. But, more importantly, they are part of the learning and understanding process. We understand that your primary goal is to learn linear algebra. While studying for the exams, you will have the opportunity to review,  make connections, and learn the hard-to-grasp concepts more deeply. In the end, this should enhance retention of your knowledge and skills. You will also be evaluated based on your exams. Please do not post or answer questions related to the exams on the discussion board  without explicit approval.

Homework On edX Platform:

There are 11 weeks for which EdX homework will be submitted. You submit a problem by clicking the “Check” box for each problem. If you miss the question, you can resubmit as many times as you wish. You can see solutions to these problems at any time in the process.

In order to help you pace yourself, we have deadlines for completing graded homework. Most of the exercises will not be graded. We hope you will find them engaging. The score for this graded homework is a completion score. The percentage of responses for a given week will be calculated upon the submission deadline for that week.

Submitted Proofs/Programs Homework:

Some proofs and programming assignments will be graded by uploading matlab files and/or pdfs on canvas. These are the problems in the “additional homework” subsections of the “wrap up”    sections at the end of each chapter – note there are no such problems in chapters 3 or 12 thus no   additional homework those weeks. Solutions to these will not be available before you submit.

We encourage discussions on Piazza after the submission period ends.

Extensions and late work:

There are no extensions in this course and absolutely no late work will be accepted under any circumstances. However, the two lowest canvas graded homework and two lowest EdX completion based homework scores will be dropped, so you have the flexibility of skipping a couple assignments when needed.

Reading your grades on canvas and EdX:

Note that the homework drops described in the previous section will be applied at the end of the semester, so you won’t see them in canvas until then. The EdX homework drops will not appear in EdX at any point, so it is possible your EdX grade will look slightly lower than your canvas grade, but it is the canvas grade that is submitted to the registrar.

Also, all canvas submitted homeworks are equally weighted, though canvas will make it look like longer homeworks are weighted higher. The calculation of homework grades with the two drops and equal weightings will also happen at the end of the semester, though you can of course do that calculation yourself at any point.

Final grade:

90- 100 A

89  A-

88  B+

80-87 B

79 B-

78 C+

60-77 C

Below 60 F

Collaboration Guidelines:

We encourage our students to collaborate, as long as the purpose is to learn. With the exception   of exams that need to be solved individually, please use the discussion boards to interact with us  and other participants. While we would like to interact with you on a more personal level, realize that we are teaching other classes as well.  We will monitor the discussion forums and will answer questions as quickly as possible.

Please help us create a healthy learning environment by following these simple standards: Be polite. Please treat one another with respect, so that everyone can continue to be involved and seek friendly support.

Be sensitive. This is a global forum with participants from many different cultures and backgrounds. Be very careful not to offend one another.

Post appropriate content. Be supportive. However, avoid posting content that violates the Honor Code or Terms of Service. You may not post inappropriate or copyrighted content, advertise or    promote outside products or organizations, or spam the forums with repeat content.

Be proud of your posts. Don’t say anything you wouldn’t want associated with your name.

Anonymous posts aren’t anonymous to staff. We do not expect to observe any issues because we trust you to keep our forum communities strong and healthy. However, inappropriate posts may   be deleted or made invisible to other students.

Overview of the class:

Linear algebra invariably lies at the core of techniques that are of critical importance to computational and data scientists. In this course, you learn advanced concepts in linear algebra,   practical algorithms for matrix computations, and how floating-point arithmetic as performed by computers affects correctness.

Statement on Learning Success:

Your success in this class is important to us. We will all need accommodations because we all learn differently. If there are aspects of this course that prevent you from learning or exclude you, please let us know as soon as possible. Together we’ll develop strategies to meet both your needs and the requirements of the course. We also encourage you to reach out to the student resources available through UT. Many are listed on this syllabus, but we are happy to connect     you with a person or Center if you would like. The University of Texas at Austin guarantees that students with disabilities have access to appropriate accommodations. You may request an accommodation letter from the Division of Diversity and Community Engagement, Services for Students with Disabilities.