SyDe 312  Applied Linear Algebra 
Stephen
Birkett

Research
Teaching
Music

Instructor: Stephen Birkett  
Teaching Assistants: Office hours TBA, TA Room E56009  
Ameneh Boroomand
 
Summary: Linear algebra is applicable and useful in ALL areas of Systems Design Engineering, and especially those which are computationally intensive: modelling and simulation, mechatronics, intelligent systems, signal analysis, image processing and computer graphics, human factors, financial and economic modelling, and software engineering. In this course you will learn mathematical concepts and techniques which generalize methods you are already familiar with, providing a solid foundation for using them in a wide array of applications. The focus here will be on how to use linear algebra, not on presenting a mathematical theory with proofs etc. , but the concepts still need to be presented carefully and precisely. In other words, the course is aimed to be APPLIED linear algebra (emphasis on the first word). Powerful computer software is now available to do numerical algebraic calculations of all kinds (Mathcad, Matlab, Maple, even handheld calculators) and you'll be expected to gain some numerical skills in this course as well. As with any math course  but this is especially true with linear algebra  the material builds on earlier work, so it is important to keep uptodate with assignment problems.  
Learning Outcomes: A) Illustrate the generality of linear algebra concepts by formulating engineering problems in a vector space context. B) Formulate and solve mathematical problems using appropriate linear algebra concepts and techniques. C) Freely use Matlab as a learning aid to support analytical problem solving and to check answers.  
Materials:  
D. Lay, Linear Algebra and its applications, AddisonWesley, 4th Edition, 2012  This is the course text. It has many worked problems as examples and a comprehensive collection of exercises for practice. It is a good textbook and worth buying. 5th edition is now available  assigned problem numbers are from the 4th edition.  
B. Bradie, A Friendly Introduction to Numerical Analysis, PrenticeHall, 2006  This book was required in 1A so you should already have it. We will use it as a supplementary text for some selected material. Author's webpage with partial answers. Author's source code archive  
Matlab  Access to Matlab is essential. Basic familiarity and skill will be assumed.  
Assistance: I don't post formal office hours because they usually turn out to be inconvenient for students for one reason or another. A more flexible arrangement seems to work best. Please contact me via email if you want to set up a meeting time. We have good TA resources too (see above) so please take advantage of that for additional assistance. Official TA help hours will be posted  they can be found in the TA room.  
Course Communications : UW Learn is inconvenient for simple communications, so I will simply send messages to the email addresses provided for the class roster by Quest. Please read these emails regularly. All announcements will be posted via this method: assignment and other course info, updates, test information, lecture and problemsolving commentary, text and lecture typos, clarifications, exam hints etc. I will identify the subject as [syde312] so you can filter them together easily.  
Outline: Most (but not all) topics are covered in the course text and/or supplementary text, but I will be hopping about a bit between the chapters in the lectures. The main units are: 1. Vector spaces, 2. Inner product spaces and orthogonality, 3. Eigenspaces and singular value decomposition. A detailed list of topics will be updated online as we go along, and also a roadmap of how the lecture topics relate to the text sections.  
Grading Scheme: Tests (40%) + research report (10%) + final exam (50%). Component weighting may be altered later at my discretion where this may be beneficial to your final grade.  
Assignments. A list of suggested relevant problems will be posted here. You can get help with these in the tutorials, from TAs, or from me. Success in math is a [nondecreasing] function of the amount of effort put into working problems (but there may be local minima), so that effort is always rewarded indirectly.  
Tests. Short elementary tests (approx 20 min) on current lecture material will be held during many (most) of the tutorials. Success requires keeping up to date in working the assigned problems. Alternate times for the tests will not be provided. To accommodate missed tests I will not count all of these in determining the test component of the grade. You should treat your test scores as a bellwether of your understanding and skill acquisition throughout the course. If you are not getting good grades things will not go better on the final exam without some intervention.  
Research report. Working in groups of two, you will investigate an applied topic or problem of your choice following the guidelines to be described in class, and prepare a short (3 page) report. The topic will involve a (nontrivial) application of linear algebra. The inside cover of the Lay textbook has MANY suggested topics, but don't be limited by these  you should choose something that you find interesting! Your report will provide an overview of the background and description of how the linear algebra concepts are applied, as well as a short original illustration with significant numerical computation content. Further details will be given in class. Your attention is drawn to Policy 71.  
Final Exam. The exam will include questions which test your: (i) knowledge of the material presented in the lectures, with an emphasis on understanding, not proofs; (ii) skill on the assigned problems and homework, and (iii) general understanding acquired. The exam will be cumulative and cover the entire course.  
Feedback. Continuous feedback is welcome. I want to know about problems while something can be done to fix them, rather than after the course is over. Don't hesitate to make suggestions for improvements. I like to hear from students about what is good or what is not good.  
Officialdom. Your attention is drawn to Policy 71. Please be aware of the campuswide policy statement on accountability with respect to course outlines.  
