**Prerequisite:**MATH 1553 or MATH 1554 or MATH 1564 or MATH 1502 or MATH 1512 or MATH 1522 or MATH 1X53.

Office hours: M10-12, W1-2, or by appointment

**Required Text:**Linear Algebra with Applications, 4th North American ed, Gilbert Strang (I plan to cover chapters 1-6, A1, and A2)

**Grading:**Bi-weekly problem sets (20%), 2 exams (40%), final exam (40%)

Final grades will be calculated as follows: A:90-100, B:80-89, C:70-79, D:60-69, F: <60

Official syllabus

**Linear algebra**is the branch of mathematics which studies vector spaces and

**linear**mappings between them. Linearity places very strong constraints on a mapping. For instance, if f: R^n->R^n is linear, then f is completely determined by evaluating it on n points. This second course will further your understanding of the ideas and methods of linear algebra, which you will learn by understanding them geometrically, justifying them algebraically, and using them to solve problems in various disciplines. You willl be required to both

**understand**all key concepts and compute with them.

Although we live in a nonlinear world, linear algebra remains an essential subject with applications throughout mathematics and its applications. There are several reasons for this, including, that in a large number of cases, useful insights about non-linear transformations can be obtained from their linear approximations. You have seen this line of thinking in calculus, where arbitrary smooth functions are studied via their derivatives - their best linear approximations. Luckily, as you will see during the course, we have a useful and beautiful theory which provides many tools to solve linear problems. The course is centered around solving four equations: Ax=b (roughly the first half of the course), A'Ax=A'b, Ax=λx, and du/dt=Au.

Gilbert Strang argues that applications of linear algebra touch many more students than calculus. These days, much of "big data" arrives in matrix form and machine learning tools utilize linear algebra. In your first linear algebra course at GT, you saw applications to Google's PageRank algorithm and Markov chains. Applications in this course will depend somewhat on student interests, but may include principal component analysis, image compression, quantum computing, solving linear ODEs and difference equations, Fourier series and transforms, graph theory, demography, etc.

**Learning Objectives**

- How to analyze and solve a linear system of equations
- Important characteristics of matrices, such as its four fundamental subspaces, rank, determinant, eigenvalues and eigenvectors
- How to factorize matrices in useful ways, such as A=LU, A=QR, diagnaliziation, Jordan form, and singular value decomposition
- Important concepts of vector spaces such as independence, basis, dimensions, orthogonality, etc. as well as sums, products, and tensor products of vector spaces
- Properties of special matrices, such as symmetric, positive definite, Hermitian, and unitary matrices
- Introduction to Matlab for linear algebra
- Applications of linear algebra in the sciences and engineering

Students with disabilities who require reasonable accommodations to participate fully in the course activities or meet course requirement are encouraged to register with the ADAPTS Disability services at 404.894.2564 or http://adapts.gatech.edu. Please