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@ -8,6 +8,8 @@ This is a repository for the course [18.065: Matrix Methods in Data Analysis, Si
**Office hours (virtual):** Thursdays at 4pm [via Zoom](https://mit.zoom.us/j/95322064681?pwd=ZzB3eHllMXpuWnNqc0NaeXZvUXF2dz09).
**Textbook**: [*Linear Algebra and Learning from Data*](https://math.mit.edu/~gs/learningfromdata/) by Gilbert Strang (2019). (Additional readings will be posted in lecture summaries below.)
**Resources**: [Piazza discussion forum](https://piazza.com/mit/spring2023/18065), [pset partners](https://psetpartners.mit.edu/).
**Grading**: 50% homework, 50% final project.
@ -32,6 +34,7 @@ useful study guide.
* ![18.065 overview diagram](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/d6b9603509fe7de36c57a0ed8ebd92d2_chart_300.jpg)
* Column space, basis, rank, rank-1 matrices, A=CR, and AB=∑(col)(row)
**Further reading**: Textbook 1.1–1.3. [OCW lecture 1](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/resources/lecture-1-the-column-space-of-a-contains-all-vectors-ax/)
## *Optional* Julia Tutorial: Wed Feb 8 @ 5pm [via Zoom](https://mit.zoom.us/j/96829722642?pwd=TDhhME0wbmx0SG5RcnFOS3VScTA5Zz09)