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1. Probability Bayesian statistics and its applications. A lot of things I want to understand like Probabilistic Graphical Models, Cryptography, and Computer V
by bhntr3 14y ago
1. Probability
Bayesian statistics and its applications. A lot of things I want to understand like Probabilistic Graphical Models, Cryptography, and Computer Vision seem to be based in fundamental bayesian statistics I never really grokked that well.
2. EE and Computer architecture.
I never studied any EE. The more I program, the more tempted I am to get closer to the metal. It's a little weird that I tell computers what to do, but I don't really understand exactly what they do with those commands. Nowadays computers are so fast, we don't NEED to know. We're network latency bound all the time. But not knowing still feels like a big gap.
3. Visualization.
There are some crazy cool alternative visualizations out there. The remainder of the decade is going to be about visualizing data for humans as we wait for the day computers can truly understand and reason about it. I want to understand the state of the art in explaining data rather than just throwing pie, line and bar charts around and calling it visualization.
All of these are interesting to me because they're core concepts. I've learned a good bit about Node, Ember, D3, CSS3, and advanced Python in my free time over the last few months, but they're just tools of the moment. I'm getting older and I want practical CS knowledge where the state of the art doesn't change fundamentally every two years. I can pick up and put down the tools.
- anujkk 14y agoI'm also interested in "3. Visualization". Can you share how are you going to approach it? I'm interested in knowing what topics to cover, which books to consult, which tools to use.
- radq 14y agoI've also been getting my hands dirty with a bit of Data Visualization lately, and have been going through the following resources: * https://graphics.stanford.edu/wikis/cs448b-12-fall/ https://graphics.stanford.edu/wikis/cs448b-12-fall/ * http://ocw.mit.edu/resources/res-6-009-how-to-process-analyze-and-visualize-data-january-iap-2012/ http://ocw.mit.edu/resources/res-6-009-how-to-process-analyz...
- bhntr3 14y agoI took Edward Tufte's course: http://www.edwardtufte.com/tufte/ http://www.edwardtufte.com/tufte/ That's interesting but most valuable is probably his books. I was planning to read Interactive Data Visualization for the Web http://ofps.oreilly.com/titles/9781449339739/index.html http://ofps.oreilly.com/titles/9781449339739/index.html I think that's mostly an introduction to the tool (D3) but maybe has some info on visualization itself. Also, this tutorial was an easy intro to D3: http://code.hazzens.com/d3tut/ http://code.hazzens.com/d3tut/ Hopefully, the author continues to add to it. Mike Bostock (author of D3) has some interesting blogs at: http://bost.ocks.org http://bost.ocks.org and also some great visualizations at http://bl.ocks.org/mbostock http://bl.ocks.org/mbostock A PHD in my office recommended The Grammar Of Graphics http://www.amazon.com/The-Grammar-Graphics-Statistics-Computing/dp/0387245448 http://www.amazon.com/The-Grammar-Graphics-Statistics-Comput... It seems much more technical and statistical but could be an interesting set of ideas to synthesize. I'm planning on borrowing his copy. Mike Bostock's stuff also led me to this page on Hive Plots which is a pretty cool description of how to do network visualization better: http://www.hiveplot.com http://www.hiveplot.com And I like Flowing Data as a blog: http://flowingdata.com/ http://flowingdata.com/ This is all of course prior to actually having studied it. I'm hoping using these resources and playing with some toy projects will lead me down further paths. (Assuming I have time which I, of course, probably won't.)