mdlthree

One could also say that agriculture changed machine learning via RA Fischer, pioneer of modern statistics - https://en.wikipedia.org/wiki/Ronald_Fisher#Rothamsted_Exper...

mdlthree

9 年前議論

https://www.youtube.com/watch?v=g8UePdbDmMw This was one of the keynote speakers at the 2015 IISE National Convention. Nancy Currie was/is works for NASA and has education in Industrial Engineering. She worked extensively investigating the Columbia space shuttle incident. Her involvement shows how industrial engineers participated in making space flight safer / better. It's really not grokking the physics that can make things better. Excellent presentation.

mdlthree

9 年前議論

Mechanical engineers make things, industrial engineers make things better. The discipline started in the 1900s mostly concerned with improving manufacturing efficiency and cost. Now it has grown to improve all facets of product quality and business process (physical and digital). It is one of the best disciplines to use applied math. https://en.wikipedia.org/wiki/Industrial_engineering

mdlthree

9 年前議論

From my experience, I would offer the generalization that nobody hires mathematicians. Mathematics in society is more of a skill set than a professional title. The most challenging or cutting edge math that could be commonly used is the LINEST() function in Excel. A person who is good at math also has a lot of great skills to offer a company, it is just selling those features and not the calculus.

I started out as a math major, then I transitioned to a double major math AND stats because stats is more applicable. I struggled for a year looking for work (also US immigration sucks, even for Canadians) and ended up in a master degree program in Industrial Engineering. I chose engineering specifically for the word "engineering". I was lucky that I discovered the field of Industrial Engineering at that university otherwise I was headed for a BS in Mechanical.

Continuing formal math education will further limit the kind of jobs you can apply, increasing the level of competition. Even the BS in Math left me with the feeling people saw me as over qualified, lacking regular skills.

Math is super great by the way, just not the idea of being a "mathematician". It (unfairly) causes alienation of your true potential.

mdlthree

11 年前議論

Video by CGP Grey on a similar topic - Humans need not apply - https://www.youtube.com/watch?v=7Pq-S557XQU

mdlthree

12 年前議論

an example of this in science - http://xkcd.com/1133/

mdlthree

12 年前議論

http://en.wikipedia.org/wiki/Central_limit_theorem

mdlthree

12 年前議論

The height vs weight chart is screaming for a linear regression line.

mdlthree

12 年前議論

This problem is like Project Euler #1. You don't strictly need to divide. I prefer to skip along the array and put in the new values.

  n,f,b= 100,3,5
  a = [str(i) for i in range(1,n+1)]
  for i in range(n/f):
      a[(i+1)*(f)-1] = 'Fizz'
  for i in range(n/b):
      a[(i+1)*(b)-1] = 'Buzz'
  for i in range(n/(f*b)):
      a[(i+1)*(f*b)-1] = 'FizzBuzz'

mdlthree

13 年前議論

I want to bring up the fact that there is a town in Texas called Canadian [http://en.wikipedia.org/wiki/Canadian,_Texas], and that is probably where a woman reported the guy. Also a county in Oklahoma, [http://en.wikipedia.org/wiki/Canadian_County,_Oklahoma].

This story has nothing to do with Canada the country.

mdlthree

13 年前議論

I think the important distinction is that he is at home in the regular environment. If you are away from home without the family, everything is different; while at home only the absence is different. This absence feels massive because of the magnitude of habits formed participating in a family unit.

mdlthree

13 年前議論

I am having trouble processing some cognitive dissonance when comparing your parable of the "Crazy Egg Enterprise Edition" but then using pricing strategies to take advantage of the sane kind of managers. While, I understand the concept of feature segmentation, I supposed should not be concerned with how other people run their business. I only need to focus on my profitability and not being a sucker.

mdlthree

13 年前議論

I have been looking for a job in manufacturing and business operations for a few years now while finishing an MS of Industrial Engineering. If I was only allowed 1 response from the prospective employer, it would be the anonymous credentials of the man/woman who did get the job.

There is a learning opportunity for me to align my expectations and my credentials to the jobs I applied to. If I could identify that major road block, I could fix it. I could apply to fewer positions knowing what was appropriate or not. Everyone else could too and there would be less resume traffic overall.

mdlthree

13 年前議論

My intention of the word introductory was not to feel like training wheels. Joeri in this thread says:

>>You have to trade off developer productivity for performance when it comes to choosing frameworks. The slower the framework the more it does for you.

And in that respect, the more the framework does for a developer, the better it is probably suited a starting point for new developers. It is a badge of success that Django and Rails can be able to serve a whole spectrum of needs.

mdlthree

13 年前議論

As an amateur hacker and after 275 days of reading Hacker News I feel I can now navigate in the sea of client side Javascript frameworks. With the introduction of this data on web framework performance, I now have another set of choices that I am completely unqualified to comprehend.

My first impression is that this data shows about 3 levels of web frameworks. At the bottom (slowest) we have Django's and Rails and many other introductory app server frameworks. Lets say after I was able to build an initial product successfully, would I then consider re-building the product in a higher performance framework as Go, Node, etc?

The third level, netty and gemini and servlet etc, I am not familiar with. Googling "netty" I get --"Netty is an asynchronous event-driven network application framework"-- I thought that is what Node is (in js) and Go does with gophers.

What are the use cases for these faster frameworks and do they follow an evolution of performance options that an app might go through?

mdlthree

13 年前議論

I tried LinkedIn Pro as a try free for 30 days. This required a credit card as it will bill you if you forget to cancel. After a week or so, I was unimpressed and canceled. Or I thought I did. $150 later and three months of not seeing the recurring transaction on my credit card I realized that I had not clicked the right buttons to cancel. I felt cheated by deceptive UI design.

mdlthree

13 年前議論

Schrodinger's cat is just like the old school intuition that is subject to errors. It is also why physics may have a better public image than math, due to the higher availability of intuitive ideas in describing physical phenomena. We wouldn't need a simultaneously alive and dead cat if people were more educated about the idea of a random variable. Heck, five seconds on wikipedia leads to "a random variable conceptually does not have a single, fixed value (even if unknown); rather, it can take on a set of possible different values, each with an associated probability."

mdlthree

13 年前議論

The Revolution:

At the turn of the 1900s, the field of mathematics was changing. The old school relied of physical intuition as a means of proof. The new school found that an adherence to formal logic led to more reliable findings.

Most Relevent Sections:

In brief, traditionalists lost the battle in the professional community but won in education. The failure of “new math” in the 1960s and 70s is taken as further confirmation that modern mathematics is unsuitable for children. This was hardly a fair test of the methodology because it was very poorly conceived, and many traditionalists were determined that it would succeed only over their dead bodies. However, the experience reinforced preexisting antagonism, and opposition is now a deeply embedded article of faith.

Many scientists and engineers depend on mathematics, but its reliability makes it transparent rather than appreciated, and they often dismiss core mathematics as meaningless formalism and obsessive-compulsive about details. This is a cultural attitude that reflects feelings of power in their domains and world views that include little else, but it is encouraged by the opposition in elementary education and philosophy.

In fact, hostility to mathematics is endemic in our culture. Imagine a conversation:

A: What do you do? B: I am a ———. A: Oh, I hate that.
Ideally this response would be limited to such occupations as “serial killer”, “child pornographer”, and maybe “politician”, but “mathematician” seems to work. It is common enough that many of us are reluctant to identify ourselves as mathematicians. Paul Halmos is said to have told outsiders that he was in “roofing and siding”!

from page 33-34.

Core methods such as completely precise definitions (via axioms) and careful logical arguments are well known, but many educators, philosophers, physicists, engineers, and many applied mathematicians reject them as not really necessary.

from page 34

Why It Matters:

The sciences are at risk in the reckless implementation of maths.

Education methods are weakened by the philosophical divide.

mdlthree

13 年前議論

I am not proud of its length, a by product of the poor vocabulary that summarizes my skills. Instead I have to list the words that are most likely to be associated with my skills. http://youtu.be/qiPc1mbRCxs

mdlthree

13 年前議論

Thanks for that link. It also did remind me of one Australian company [http://www.biarri.com/about_the_team.shtml] that I explore from time to time. They seem to offer the most types of OR decision support for business, even with SaaS delivery.

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