ENGR108: Introduction to Matrix Methods
Syllabus
In this course, we will focus (of course) on vectors, matrices, and
their applications, with a special emphasis on those we can understand
and solve via leastsquares problems. Lectures will consist of going
through slides, on which we will actively take notes, and we will
post the handwritten notes here.
A rough outline of topics we expect to cover is as follows:
Vectors: definitions, operations on vectors, distances, norms, and
applications in clustering.
Matrices: definitions, examples, basic factorizations and their uses,
examples via dynamical systmes.
Lectures and Reading
Here, we will post readings from the
course textbook associated with
each lecture. In addition, for posterity's sake, we post a few annotated
lecture slides from previous iterations of the course, though
their similarity with this year's version may vary.
 Topic  Reading  Slides 
Tue, April 4  Overview and vectors  Ch. 1  Overview, vectors 
Thu, April 6  Vectors and linear functions  Chs. 1–2  vectors, linear functions 
Tue, April 11  Linear functions and norms  Chs. 2–3  linear functions 
Thu, April 13  Norms and clustering  Chs. 3–4  norms 
Tue, April 18  Clustering  Ch. 4  clustering 
Thu, April 20  Linear independence  Ch. 5  linear independence 
Tue, April 25  Bases and Gram Schmidt  Ch. 5  linear independence 
Thu, April 27  Matrices  Ch. 6  matrices 
Tue, May 2  Matrices  Ch. 6  matrices 
Thu, May 4  Midterm  Chs. 1–6  
Tue, May 9  Matrix examples  Ch. 7  examples 
Thu, May 11  Matrix examples  Ch. 7  examples 
Tue, May 16  Linear equations  Ch. 8  equations 
Thu, May 18  Matrix multiplication  Ch. 10  multiplication 
Tue, May 23  Matrix multiplication and inverses  Chs. 10–11  multiplication, inverses 
Thu, May 25  Matrix inverses  Ch. 11  inverse 
Tue, May 30  Least squares  Ch. 12  least squares 
Thu, June 1  Least squares data fitting  Ch. 13  fitting 
Tue, June 6  Least squares data fitting  Ch. 13  fitting

