Welcome to the homepage for Linear Algebra MA283 in the 22/23 academic year.
This course will run over the 12 weeks of Semester 2, with the first lecture on January 11. It is an introduction to the concepts, methods and applications of linear algebra, for students specializing in mathematics and related subject areas.
Content will be posted week by week at the links below.
Wednesday 1.00–1.45, G002 Áras Uí Cathail
Friday 11:00–11:45, D’Arcy Thompson lecture theatre
Tutorials will commence in Week 4, at the following times and venues.
- Monday 2-3pm: ENG-2033
- Monday 3-4pm: ENG-2033
- Tuesday 5-6pm: Tyndall
The module tutor is Victoria Sánchez Muňoz who is a research student in the School of Maths.
This course is an introduction to Linear Algebra, which is the study of vector spaces. Vector spaces are algebraic structures (sets) whose elements can be added, subtracted or multiplied by scalars (numbers). The algebra of matrices is closely related to linear algebra, and provides a concrete framework and a comprehensive computational environment for studying the subject. Much of our work will be concerned with matrices and their properties, but we will also be interested in the wider context of abstract vector spaces.
The concepts and methods of Linear Algebra is absolutely pervasive and have fundamental importance in every area of the mathematical and statistical sciences. Matrix algebra is a powerful tool in the mathematical modelling of physical and dynamic processes in both discrete and continuous contexts. Because linear algebra is relatively well understood and particularly amenable to computational methods, it has huge importance in scientific computing as well as in numerical analysis and approximation theory. It is also indispensable in geometry, combinatorics, analysis and all areas of algebra.
Our syllabus will have four chapters.
- What is linear algebra about?
Matrices, review of matrix algebra, systems of linear equations.
- Spanning sets, bases and dimension
Linear independence, bases and dimension. The rank of a matrix. The kernel and image of a linear transformation. The Rank-Nullity Theorem.
- Linear Transformations, Eigenvectors and Similarity
Choice of basis and change of basis. Diagonalizabilty. Eigenspaces.
- Orthogonality, inner products and projections
Some additional useful structure in vector spaces.
Course and Lecture Notes
The lecture notes form the main text for the course. They will be posted in instalments here as we proceed.
|Chapter 1 What is linear algebra about?|
|Chapter 2 Vector spaces, spanning sets, bases and dimension|
|Chapter 3 Linear transformations, eigenvectors and similarity|
|Chapter 4 Inner products, orthogonality and projections (updated March 28)|
Assessment of students’ learning in MA283 will consist of the following elements.
- Three webwork homework assignments (30%)
- Two-hour final exam in the summer exam session (70%)
- Optional bonus: ten weekly challenges run via Blackboard (2% each, 20% total)
Each challenge will ask students to provide an example of some object with specified properties. Submissions will be marked out of 10 points, with 5 for mathematical accuracy and 5 for creativity and quality of exposition.
As you can see, this adds up to more than 100%. Total scores that exceed 100% will be returned as 100%.