Readings & Resources
Influential Friends, Books, and Articles
Readings and Resources: Statistical Mechanics, Neural Networks, and Machine Learning
“A man is known by the books he reads, by the company he keeps…” – Ralph Waldo Emerson
The following links contain my own top-pick reads, with a goal of providing context and background. Suggested as “between-terms” readings for my students in Northwestern University’s Master of Science in Predictive Analytics program, as well as for colleagues, faculty, and friends.
Feedback and suggestions are always welcome. Send an email under header “Bookshelf Suggestion” to me at: alianna.maren (at) northwestern (dot) edu. And thank you!
New Stuff – Mind-boggling:
Quanta on new theory regarding deep learning
Quanta – building a brain from silicon switches – neuromorphic
AJ’s Notes to Self – for later follow-up and better sorting:
Note to self: Answer this when I complete the Machine Learning Intro series
Readings and Resources – Artificial Intelligence
I’m pulling out a small collection of knowledge representation articles because knowledge representation is one of the key underlying AI principles. In fact, I plan to start off both of my classes this quarter (Text Analytics and AI & Neural Networks / Deep Learning) with some knowledge representation exercises, just to see if we can’t get a better handle on this topic, earlier.
- Readings – AI – Knowledge Representation: Readings – Knowledge Representation Starter Set.
Readings and Resources – Deep Learning Self-Study
- The Armchair Traveler: You want to read ABOUT neural networks, deep learning, and machine learning – but are not ready to do the equations and the code. Or, you’re just looking for something to download and listen to while you take the kids on a roadtrip, or while doing the daily commute. Includes survey-level books and articles.
- Go to No-Equations AI Reading (and science fiction bonus link): AI reading that you can listen to while you drive – or read while you’re going to sleep. Some of these books (maybe all) are available on iTunes as podcasts.
- A few applications-oriented survey articles: deep learning and AI surveys and overviews, including industry trends,
- Something really new, every 32 years: A brief history of AI and neural networks, and why we’re ready for something new, right now: a brief history; 64 years and two eras of AI and neural networks.
- Starting Out: What I would read if I were starting today
- Great Plains (arid and dry, just working through it): Deep learning – (slightly) more advanced.
- Rocky Mountains (the challenging math): Energy-based methods in deep learning.
- Looking over my shoulder: Dr. A.J.’s personal reading list
Readings and Resources – Python
Just one link so far …
- Readings – Python for Deep Learning: Readings – Python – transition to object-oriented
… and no, I don’t have all the goodies for getting into TensorFlow, etc., up yet. Maybe this quarter.
Readings and Resources – Text Analytics
Just added a new page on text analytics applications:
- Readings – Text Analytics: Readings – Text Analytics Starter Set
- Readings – Text Analytics: Readings – Text Analytics: Practical Implications and Applications
Research – Papers & Books, Organized by Topic
This is my own private stash. It’s given here so that I have easy access to articles that are relevant to my own research, and is a very ad-hoc collection. You’re welcome to peruse and enjoy!
The links on this page connect to my “reading stash” – my personal online collection of research papers and books:
- Classic/seminal and I’ll refer to them again and again,
- Substantial review papers, and even those I like because of their reference lists,
- Noteworthy articles – sometimes part of a small topical collection – that make important points,
- My current reading list, for easy access (but not always well-organized), and
- Slush-pile – links to papers that I (may) want to read (someday, even soon), but are not on my current reading stack.
- Looking over my shoulder: Dr. A.J.’s personal reading list
- Biological Sensor Fusion and Visual Processing (with Machine Vision Applications),
- Brain-Computer Interfaces,
- Computational Neuroscience,
- Energy-based methods in deep learning (AJM’s Note: This is the same Reading List as the Rocky Mountains one for deep learning, above),
- Statistical physics applied to neuroscience,
- Statistical physics & information theory,
- Neuroscience,
- Neurology – Imaging,
- Websites,
- Articles & Other Resources.