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. 

The way to have good ideas is to get close to killing yourself. It’s  like weightlifting. When you lift slightly more than you can handle, you  get stronger.
In life, when the gun is to your head, you either figure it out, or you die.
When you cut yourself open, you bleed ideas. If you’re broke and close to death, you have to start coming up with ideas.
If you destroy your life, you need to come up with ideas to rebuild it.
The only time I’ve been FORCED to have good ideas is when I was up  against the wall. My life insurance policy was like a gun to my head:  “Come up with good ideas… OR ELSE your kids get your life insurance!”
At an airport when I realized a business I had been working on for four years was worthless.
Or when I was sitting in the dark at three in the morning in the living  room of the house I was going bankrupt and losing my home, my brain figuring out how to die without anyone knowing it was planned.
Or when I was getting a divorce and I was lonely and afraid I wouldn’t  make any money again or I wouldn’t meet anyone again. Or my kids would  hate me. Or my friends would be disgusted by me.
The problem is  this: you’re NOT in a state of panic most of the time. States of panic  are special and have to be revered. Think about the times in your life  that you remember – it’s exactly those moments when you hit bottom and  were forced to come up with ideas, to get stronger, to connect with some  inner force inside you with the outer force.
This is why it’s important NOW to strengthen that connection to that idea force inside of you. This post is about HOW.
Nothing you ever thought of before amounted to anything – that’s why  you are exactly where you are at that moment of hitting bottom. Because  all of your billions of thoughts have led you to right there. You can’t  trust the old style of thinking anymore. They came, they saw, they lost.
You have to come up with a new way of thinking. A new way of having ideas. A new ways of interacting with the outside universe.
You’re in crisis. Time to change. Time to become an IDEA MACHINE.

Quartz tells us why we love lip sync battles.

Jonah Leher
A new paper by a team of scientists at the University of Minnesota (Chiraag Mittal, Vladas Griskevicius, Jeffry Simpson, Sooyeon Sung and Ethan Young) investigates this surprising hypothesis. The researchers focused on two distinct aspects of cognition: inhibition and shifting.
Inhibition involves the exertion of mental control, as the mind overrides its own urges and interruptions. When you stay on task, or resist the marshmallow, or talk yourself out of a tantrum, you are relying on your inhibitory talents. Shifting, meanwhile, involves switching between different trains of thought. “People who are good at shifting are better at allowing their responses to be guided by the current situation rather than by an internal goal,” write the scientists. These people notice what’s happening around them and are able to adjust their mind accordingly. Several studies have found a correlation between such cognitive flexibility and academic achievement.
The researchers focused on these two cognitive functions because they seemed particularly relevant in stressful childhoods. Let’s start with inhibition. If you grow up in an impoverished environment, you probably learn the advantages of not waiting, as delaying a reward often means the reward will disappear. In such contexts, write the scientists, “a preference for immediate over delayed rewards…is actually more adaptive.” Self-control is for suckers.
However, the opposite logic might apply to shifting. If an environment is always changing – if it’s full of unpredictable people and intermittent comforts – then a child might become more sensitive to new patterns. They could learn how to cope by increasing their mental flexibility. 

Quartz
On selfish people:
This implies that people who grow up in families where selflessness is rewarded will develop an instinctively cooperative approach. Similarly, companies that do not deter employees from only thinking of themselves will likely have a selfish staff—even in cases where refusing to cooperate is actively harmful.
“It applies to interactions between friends, coworkers, family members—all interactions where you have the chance to do something that’s costly for you but beneficial for other people,” says Rand.
Cross-culturally, it suggests that those who grow up in countries without a strong rule of law will develop an uncooperative intuition, he adds.
Which means that if you meet an instinctively selfish person, reasoning is unlikely to persuade them to cooperate. But if you are that selfish person, you can blame evolution for your bad behavior.

Quartz
Vincent Tinto is a professor at Syracuse University. He’s well known for his theories on students’ persistence through higher education. His research produced what’s known as the “Model of Institutional Departure.”
Tinto’s model informs us that, above all else, college is a transition from one community to another. Our success in college depends on how well we integrate ourselves into that new community.

What happens if we go home every weekend to visit high-school friends and sweethearts instead of making friends in our new community? We don’t integrate. We don’t get help from new friends going through the same issues or receive academic advice from new mentors. Instead, we tend to drop out.
Tinto’s model has proven incredibly useful in improving how we educate not just undergraduates but people enrolled in remote learning programs and continuing-education classes. And it can be extended far past education to help us understand how people persist.

​

Notre Dame psychologist and computer scientist D’Mello, whose research areas include artificial intelligence, human-computer interaction and the learning sciences, together with Art Graesser of the University of Memphis, collaborated on the study, which was funded by the National Science Foundation.
They found that by strategically inducing confusion in a learning session on difficult conceptual topics, people actually learned more effectively and were able to apply their knowledge to new problems.

Scientific American

The subjects watched an animated tutor and student discuss possible flaws in a scientific study. The researchers had the animated tutor and student disagree with each other on specific flaws.  But to set up a really confusing situation for one group of subjects they also had the pretend tutor and student make incorrect or contradictory statements about the study. Then the subjects had to decide which of the two opinions had more scientific merit.
Subjects who were forced to deal with the incorrect and contradictory statements did significantly better on later tests where they had to spot flaws in studies, as opposed to those subjects who only faced the disagreements between the animated tutor and student. The study will be published in the journal Learning and Instruction.
Researchers note that confusion motivates us to work harder to understand, and so we gain a deeper and more comprehensive knowledge of a subject.

Quartz
Students who are presented with unfamiliar concepts, asked to work through them, and then taught the solution significantly outperform those who are taught through formal instruction and problem-solving. The approach is both utterly intuitive—we learn from mistakes—and completely counter-intuitive: letting kids flail around with unfamiliar math concepts seems both inefficient and potentially damaging to their confidence.
Kapur believes that struggle activates parts of the brain that trigger deeper learning. Students have to figure out three critical things: what they know, the limits of what they know, and exactly what they do not know. Floundering first elevates the learning from knowing a formula to understanding it, and applying it in unfamiliar contexts.

Quartz
Creative thinking requires our brains to make connections between seemingly unrelated ideas. Is this a skill that we are born with or one that we develop through practice? Let’s look at the research to uncover an answer.

In the 1960s, a creative performance researcher named George Land conducted a study of 1,600 five-year-olds. Ninety-eight percent of the children scored in the “highly creative” range. Dr. Land re-tested each subject at five year increments. When the same children were 10 years old, only 30% scored in the highly creative range. This number dropped to 12% by age 15 and just 2% by age 25. As the children grew into adults they effectively had the creativity trained out of them. In the words of Dr. Land, “non-creative behavior is learned.”

Similar trends have been discovered by other researchers. For example, one study of 272,599 students found that although IQ scores have risen since 1990, creative thinking scores have decreased.

​This is not to say that creativity is 100% learned. Genetics do play a role. According to psychology professor Barbara Kerr, “approximately 22% of the variance [in creativity] is due to the influence of genes.” This discovery was made by studying the differences in creative thinking between sets of twins.