Udacity
https://www.udacity.com/courses/web-development
http://www.charlierose.com/watch/60666797


Machine Learning and other resources (Thanks Murray):

Stanford
https://www.coursera.org/learn/machine-learning

The course is taking into consideration that a lot of the students have very little background in math and statistics, so some of the material will seem too easy for you. With that said, the value is in seeing how to develop the mechanisms to proceed from a conceptual understanding of a least-squares regression (for the beginner example) and produce your beta coefficients that STATA or R would be giving you...Or, how do you actually tell a computer to minimize your MSE, etc.

Once you access the material you'll see that you can move ahead as fast as you want, so it is self paced.

I think you'll find a lot of the content super interesting and I think you'll quickly see ways of how to put the material into practice for yourself.

Here is an overview of the curriculum:

Course
WEEK 1
Introduction
Linear Regression with One Variable
Linear Algebra Review
WEEK 2
Linear Regression with Multiple Variables
Octave Tutorial
WEEK 3
Logistic Regression
Regularization
WEEK 4
Neural Networks: Representation
WEEK 5
Neural Networks: Learning
WEEK 6
Advice for Applying Machine Learning
Machine Learning System Design
WEEK 7
Support Vector Machines
WEEK 8
Unsupervised Learning
Dimensionality Reduction
WEEK 9
Anomaly Detection
Recommender Systems
WEEK 10
Large Scale Machine Learning
WEEK 11
Application Example: Photo OCR

I've been humm'ing and haa'ing about Udacity since I stumbled across it a few weeks ago. I will probably do the Machine Learning Udacity courses because it combines high-level thinking, programming, and [math and statistics] education. For the forseeable future, I think there will be a higher demand for guys (and girls) who can cover those bases. I mean, I've done a lot of reading and came across a group of guys at mlwave.com who can seemingly solve a wide array of problems from predictingwhich Grandmaster played x chess-game to a fascinating (but incomplete) tutorial on using timeseries analysis to predict neuronal firing patterns in a zebrafish embryo. Tell your students this is why they should pay attention in Econometrics!!

Here is a list of the better resources/things I've found:

Stanford Machine Learning - In the community this is considered your square-one for Machine Learning.
Udacity's Nanodegree - For redundancy: can't leave this out for obvious reasons.
Codecademy.com - This is the site I used to get a feel for the syntax of Python
University of Washington - Machine Learning Specialization - I am in a couple of courses from this specialization. Apparently they have a pretty good reputation and are respected. I am trying to get away from UofW because I personally don't like the way their assignments are setue because they use certain code libraries that you have to pay for once you're done the course. I don't see it as valuable in the long run to use things that aren't open-source.
*Kaggle.com - Popular website for budding data scientists, programmers, math-heads, etc., to try their hand at competitions to solve some cutting-edge data problems. I'm currently using Kaggle tutorials to teach myself.
Data Science iPython Notebooks - Amazing resource for code examples and tutorials on some data science and Machine Learning stuff including Kaggle and Tensorflow materials.
Tensorflow.org - In late 2015, Google open-sourced their Machine Learning and AI library. A lot of the better Kaggle tutorials seem to be using them now and their capabilities are pretty mind-blowing.
A Tour of Machine Learning Algorithms - Good Summary of different Machine Learning Algorithms.
Livecoding.tv - A website with streaming and archived programmers doing their thing. Haven't watched many but I think I should.

P.S. Can't send an Email like this without a plug for Watson. 

Quartz
Have you thought of “technological unemployment,” where machines take all our jobs?

The outcome will depend on how things are distributed. Everyone can enjoy a life of luxurious leisure if the machine-produced wealth is shared, or most people can end up miserably poor if the machine-owners successfully lobby against wealth redistribution. So far, the trend seems to be toward the second option, with technology driving ever-increasing inequality.


Here is an example via Business Insider or 3D Printing of Bridges in Amsterdam

My thanks to Warren and Tiara. The former suggests that the already steep rising trend in inequality is set to continue and maybe even accelerate. The latter suggests that even in rigged games those at the top feel more entitled and become more unethical. This suggests Marx's view of "inevitable" class conflict maybe the most correct forecast of the future.

Sounds about right by proportions. Via Motherboard

Via Conversable Economist. My thanks again to Brent. Brent sums up a key graph as follows:

"Presented that way (as share of total employment), there's an evident steady trend in U.S. manufacturing employment.
Alternatively, one could present something like annual percentage job loss in the U.S. manufacturing sector and see an accelerating trend."

Charlie Rose interviews  Time Magazine's Lev Grossman. D-Wave, a  Canadian firm, claims to make the first commercial product, that sells for $10,000,000, and is cooled at near absolute zero. Applications for machine learning.

Kneel before your robot overloard. Technology marches on - routine jobs will continue to be eliminated. 60 Minutes just did a piece on the skills gap. There are unfilled jobs in manufacturing in America but they involve programing computers. The jobs profiled pay $12/hr in Nevada with benefits. Expect inequality, if left unchecked, to continue to increase. 

Business Insider.
"Moving on, Chanos tackled an even bigger storyline on Wall Street — the  belief that the economy is being hurt by too much regulation and uncertainty.   This narrative just "doesn't hold" when you look at the numbers, Chanos said.   For example, The Fed Register of financial regulation has grown just as much  under President Obama as it did under President Bush.

 And as for investment: "They say people are holding back investment ... and   again, the numbers belie that," said Chanos. "Domestic GDP investment in the   U.S. is back up just about to pre-recession levels and bottomed out in 2009,   2010 ..."

 The problem, he said, is actually that "the investment we're all looking for  is actually saving labor ... Look at what the internet is doing to retail," he  added.

 According to Chanos, investing in technology that makes things more efficient  doesn't save jobs. So we're looking at a capital-labor tradeoff that's been   going on for years. In the early years of the Bush administration this was   masked by strong construction jobs but since the housing market collapsed, those  are gone."

Long time readers of this blog will know this lines up pretty well with my views on structural unemployment.
Those wishing to see the video on Bloomberg.