AI News, Locklin on science
- On Monday, June 4, 2018
- By Read More
Locklin on science
They all mention regression models, logistic regression, neural nets, trees, ensemble methods, graphical models and SVM type things.
Sometimes I am definitely just whining that people don’t pay enough attention to the things I find interesting, or that I don’t have a good book or review article on the topic.
If you’re not thinking about how you’re exposing your learners to sequentially generated data, you’re probably leaving information on the table, or overfitting to irrelevant data.
which strike me as being of extreme importance, though this is a presentation of new ideas, rather than an exposition of established ones. Vowpal Wabbit is a useful and interesting piece of software with OK documentation, but there should be a book which takes you from online versions of linear regression (they exist!
Hell, I am at a loss to think of a decent review article, and the subject is unfortunately un-googleable, thanks to the hype over the BFD of “watching lectures and taking tests over the freaking internets.”
The problem is, the guys who do reinforcement learning are generally in control systems theory and robotics, making the literature impenetrable to machine learning researchers and engineers.
I can’t give you a good reference for this subject in general, though Ron Begleiter and friends wrote a very good paper on some classical compression learning implementations and their uses.
Even in marketing problems dealing with survival techniques, there is a time component, and you should know about it.In situations where there are non-linear relationships in the time series, classical regression and time-series techniques will fail.
In situations where you must discover the underlying non-linear model yourself, well, you’re in deep shit if you don’t know some time-series oriented machine learning techniques. There was much work done in the 80s and 90s on tools like recurrent ANNs and feedforward ANNs for starters, and there has been much work in this line since then.
There are plenty of other useful tools and techniques. Once in a while someone will mention dynamic time warping in a book, but nobody seems real happy about this technique. Many books mention Hidden Markov Models, which are important, but they’re only useful when the data is at least semi-Markov, and you have some idea of how to characterize it as a sequence of well defined states.
Even in this case, I daresay not even the natural language recognition textbooks are real helpful (though Rabiner and Juang is OK, it’s also over 20 years old).
This isn’t exactly a cookbook or exposition, mind you: more of a thematic manifesto with a few applications. Obviously, signal processing has something to say about the subject, but what about learners which are designed to function usefully when we know that most of the data is noise? Fields such as natural language processing and image processing are effectively ML in the presence of lots of noise and confounding signal, but the solutions you will find in their textbooks are specifically oriented to the problems at hand. Once in a while something like vector quantization will be reused across fields, but it would be nice if we had an “elements of statistical learning in the presence of lots of noise”
Feature engineering: feature engineering is another topic which doesn’t seem to merit any review papers or books, or even chapters in books, but it is absolutely vital to ML success.
A review article or a book chapter on this sort of thing, thinking through the relationships of these ideas, and helping the practitioner to engineer new kinds of feature for broad problems would be great.
Unsupervised and semi-supervised learning in general: almost all books, and even tools like R inherently assume that you are doing supervised learning, or else you’re doing something real simple, like hierarchical clustering, kmeans or PCA. In the presence of a good set of features, or an interesting set of data, unsupervised techniques can be very helpful.
- On Thursday, January 17, 2019
Hello World - Machine Learning Recipes #1
Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's ...
11. Introduction to Machine Learning
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: Instructor: Eric Grimson ..
How to speak so that people want to listen | Julian Treasure
Have you ever felt like you're talking, but nobody is listening? Here's Julian Treasure to help you fix that. As the sound expert demonstrates some useful vocal ...
Cheat Sheet is Here : Slower Java Tutorial : How to Install Java & Eclipse : Best Java Book
SMART Boards Why are they so easy to use?
Watch teachers and students demonstrate what makes the SMART Board so easy to use, and hear what teachers have to say about how SMART products are ...
Machine Learning Techniques and Applications in Finance, Healthcare and Recommendation Systems
David Vogel, Trustee (Voloridge Investment Management, LLC) Abstract: The introductory portion of this talk will review some state-of-the-art machine learning ...
Building a Recommendation Engine with Machine Learning Techniques (Brian Sam-Bodden) - FSF 2016
In this talk Brian will walk you through the ideas, techniques and technologies used to build a SaaS Recommendation Engine. From building an efficient ...
This video lecture explains how to put a report together as an assignment, and focuses on the elements which are required in a good report.
How to write a good essay
How to write an essay- brief essays and use the principles to expand to longer essays/ even a thesis you might also wish to check the video on Interview ...
Get the Cheat Sheet Here : Best Book on Python : Beginner Python Tutorial .