ENGR108: Introduction to Matrix Methods
Syllabus
The complete, live-updating syllabus document is available here.
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 least-squares 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. We only have 8 weeks in the summer quarter, as opposed to 10 in
a typical academic quarter, so we are aiming to move at a quick pace, and will
update the schedule as needed.
| Topic | Reading | Slides |
M 6/24 | Overview and vectors | Ch. 1 | blank, annotated |
W 6/26 | Linear functions | Ch. 2 | blank, annotated |
M 7/1 | Norms | Ch. 3 | blank, annotated |
W 7/3 | K-means clustering | Ch. 4 | blank, annotated |
M 7/8 | Linear independence and bases | Ch. 5 | blank, annotated |
W 7/10 | Matrices | Ch. 6 | blank, annotated |
M 7/15 | Matrix examples | Ch. 7 | blank, annotated |
W 7/17 | More matrix examples, exam review | Ch. 7 | blank, annotated |
M 7/22 | Midterm | Chs. 1–6 |
W 7/24 | Linear function models | Ch. 8 | blank |
M 7/29 | Matrix multiplication | Ch. 10 |
W 7/31 | Linear dynamical systems | Ch. 9 |
M 8/5 | Matrix inverses | Ch. 11 |
W 8/7 | Least squares | Ch. 12 |
M 8/12 | Least squares data fitting | Ch. 13 |
W 8/14 | More data fitting, exam review | Ch. TBD
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