Readings & Resources

Readings & Resources

Influential Friends, Books, and Articles

 

Readings and Resources: Statistical Mechanics, Neural Networks, and Machine Learning

 

Ralph Waldo Emerson
Ralph Waldo Emerson, American essayist, lecturer, and poet (1803-1882).

“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!

 

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 and Resources – Deep Learning Self-Study

 

 

Readings and Resources – Python

 

Just one link so far …

… and no, I don’t have all the goodies for getting into TensorFlow, etc., up yet. Maybe this quarter.

 

Readings and Resources – Text Analytics

 

The links here are a bit old and sparse; over the fall quarter (2017), I will add materials from my Text Analytics course at Northwestern University to this list.

 

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:

  1. Classic/seminal and I’ll refer to them again and again,
  2. Substantial review papers, and even those I like because of their reference lists,
  3. Noteworthy articles – sometimes part of a small topical collection – that make important points,
  4. My current reading list, for easy access (but not always well-organized), and
  5. Slush-pile – links to papers that I (may) want to read (someday, even soon), but are not on my current reading stack.