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Data-Driven Science and Engineering 2nd Edition [pdf]
- mindcrime 3y agoAnnouncement video: https://www.youtube.com/watch?v=oEXR9EnAtm4 https://www.youtube.com/watch?v=oEXR9EnAtm4 Code can be found at: https://github.com/dynamicslab/ https://github.com/dynamicslab/ More info / video links /etc (from 1e): http://databookuw.com/ http://databookuw.com/
- gp7 3y agoVery good youtube channel, if anyone is wondering
- Joel_Mckay 3y agoHighly recommended book, they also have a paper copy if you want to support their work. Very pleasing to see other people noticed they updated the text. https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical-dp-1009098489/dp/1009098489/ref=dp_ob_title_bk https://www.amazon.com/Data-Driven-Science-Engineering-Learn... Have a wonderful day =)
- mohaba 3y agoFYI, huge PDF. $ ls -sh DataBookV2.pdf 103M DataBookV2.pdf
- loeg 3y agoCanonical UW URLs use the uw.edu domain: https://faculty.uw.edu/sbrunton/DataBookV2.pdf https://faculty.uw.edu/sbrunton/DataBookV2.pdf (huge 100MB PDF, as others have noted). (Washington got pretty lucky to be granted this domain and universities are no longer being granted contractions like this. Wisconsin and Waterloo might be somewhat jealous.)
- adiM 3y agoOnly Wisconsin. Canadian universities usually have .ca rather than .edu
- quanto 3y agoI am looking for a book and resource for (advanced) data science. I understand there are numerous ML books and articles discussing algorithms at all levels; there are also numerous introductory books on data science, e.g. how to call functions in sklearn. What I am looking for is a book on data exploration, data modeling, and prediction error analysis; with some level of rigor in both theory and implementation. Any recommendation for such a book or resource? DatabookV2 discussed here looks great for natural science + ML. It reminds me of this widely-cited paper A high-bias, low-variance introduction to Machine Learning for physicists https://arxiv.org/abs/1803.08823 https://arxiv.org/abs/1803.08823
- nextos 3y agoSee this MLSS '20 presentation and all recommended bibliography [1], in particular top left box in slide 123. For rigorous modeling, Gelman et al. books are great [2-3]. [1] https://drive.google.com/file/d/1lPePNMGMEKoaDvxiftc8hcy-rFpobXeJ/view https://drive.google.com/file/d/1lPePNMGMEKoaDvxiftc8hcy-rFp... [2] https://avehtari.github.io/ROS-Examples https://avehtari.github.io/ROS-Examples [3] http://www.stat.columbia.edu/~gelman/book http://www.stat.columbia.edu/~gelman/book
- quanto 3y agoAh, thank you. I did hear about Gelman's books before, but I haven't read them in detail. Will check those out. Do you have references/books for non-Bayesian approach as well?
- nextos 3y agoYou could try this one: https://hastie.su.domains/CASI_files/PDF/casi.pdf https://hastie.su.domains/CASI_files/PDF/casi.pdf However, IMHO many things come more naturally in a Bayesian setting, such as hierarchical models.
- petulla 3y agoESLR then Murphy ML: A PP
- npalli 3y agoA bit confused with the "announcement", this book has been out for a while. I have the paper copy from almost 8 months ago. Did something change? In any case, as others have noted very good book.
- mindcrime 3y agoI don't know, I just saw this hit my YT recommendations a few hours ago. I had no idea there was a 2E out. But I'm glad this did show up when it did, as I had the 1E parked in my cart at Amazon and was close to ordering a copy. Now I'll make sure to get the newer version.
- windsignaling 3y agoSteve Brunton has an awesome YouTube channel as well. For those in the data science / machine learning space, it's very interesting to see those techniques applied to control and other areas of engineering - things they never taught us in engineering school!
- thelastbender12 3y agoThe co-author Nathan Kutz is a great instructor too. I recall going through his optimization course on Coursera back when MOOCs were new; instructors excited about the course material make learning fun.