Calgary Herald

​Another bright spot has been the results of new fluids Crescent Point has been using to help extract oil from the Viewfield Bakken formation in southeastern Saskatchewan. In tests last year, the fluids improved initial 90-day oil production rates by about 50 per cent.Saxberg did not divulge details about the fluids because they are "proprietary."
"Early results are pretty impressive," he said. "It's pretty exciting stuff in this environment and I think the low-cost environment allows us to do this experimentation and we probably put $50 million of our budget across all of our fields toward that technology."

​Only a matter of time before this diffuses to rest of industry.

 

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.