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@ -20,7 +20,7 @@ This is a repository for the course [18.065: Matrix Methods in Data Analysis, Si
* **1-page** proposal **due Monday April 3** [on Canvas](https://canvas.mit.edu/courses/18680) (right after spring break), but you are encouraged to discuss it with Prof. Johnson earlier to get feedback.
* Pick a problem involving "learning from data" (in the style of the course, but not *exactly* the same as what's covered in lecture), and take it further: to numerical examples, to applications, to testing one or more solution algorithms. Must include computations (using any language).
* Final report **due May 15**, as an 8–15 page academic paper in the [style template](https://template-selector.ieee.org/secure/templateSelector/format?publicationTypeId=1&titleId=154&articleId=1) of [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://www.computer.org/csdl/journal/tp).
* Like a good academic paper, you should **thoroughly reference** the published literature (citing both original articles and authoritative reviews/books where appropriate \[rarely web pages\]), tracing the historical development of the ideas and giving the reader pointers on where to go for more information and related work and later refinements, with references cited throughout the text (enough to make it clear what references go with what results). (Note: you may re-use diagrams from other sources, but all such usage must be _explicitly credited_; not doing so is [plagiarism](http://writing.mit.edu/wcc/avoidingplagiarism).)
* Like a good academic paper, you should **thoroughly reference** the published literature (citing both original articles and authoritative reviews/books where appropriate \[rarely web pages\]), tracing the historical development of the ideas and giving the reader pointers on where to go for more information and related work and later refinements, with references cited throughout the text (enough to make it clear what references go with what results). (Note: you may re-use diagrams from other sources, but all such usage must be _explicitly credited_; not doing so is [plagiarism](http://writing.mit.edu/wcc/avoidingplagiarism).) See some [previous topic areas](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/pages/final-project/).
What followes is a *brief* summary of what was covered in each lecture, along with links and suggestions for further reading. It is
*not* a good substitute for attending lecture, but may provide a
@ -41,4 +41,4 @@ A basic overview of the Julia programming environment for numerical computations
* [Tutorial materials](https://github.com/mitmath/julia-mit) (and links to other resources)
If possible, try to install Julia on your laptop beforehand using the instructions at the above link. Failing that, you can run Julia in the cloud (see instructions above).
If possible, try to install Julia on your laptop beforehand using the instructions at the above link. Failing that, you can run Julia in the cloud (see instructions above).