
Enrollment Is Surging in Machine Learning Classes - Osiris30
http://blogs.nvidia.com/blog/2016/02/24/enrollment-in-machine-learning/
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tfgg
Based on recent (successful) job interviewing, I'd recommend people looking
for a job in data science/ML to do a statistical learning course such as the
Hastie and Tibshirani Stanford one [1] as a higher priority over ML/deep
learning courses. It gives you a base level of knowledge in the field, and
even for jobs that do deep learning, most of the technical questions will be
about making sure you know the classical concepts really well.

[1]
[https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...](https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/info)

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KevinNinja
How difficult is it to lend AI engineer/Data engineer (fresh grad) position
for someone without masters/Ph.D? What do you recommend to person like this?

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ugh123
While I disagree with stuxnet79 about needing an advanced degree, you'll
likely not find much without some kind of experience. In lieu an MS or PhD,
you may want to start in entry-level development at a shop that also has some
machine learning, big data analysis group and work your way in their over the
course of a few years. After a few years you may be in a better position,
experience-wise, than many M.S. grads i've seen.

Goodluck

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IanOzsvald
To add some evidence - I co-chair PyDataLondon (3,000 members, UK's largest
Python u/g, UK's most active data science group). I survey our members, our
monthly attending group are 40% PhD, 40% MSc, 20% other, few have 5-10 yrs
industry experience, the majority have 2-4 years. I'd argue that you need at
least a relevant MSc + a couple of years experience to begin to talk of being
a data scientist/AI engineer. Coming through data engineering in support of
data science is a great route to get practical experience where there's a lot
of job demand, at least in London.

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KevinNinja
Thanks for input. I have a BS in CS. I took couple of AI/ML centric courses in
my undergrad. I worked on couple of ML centric open source projects, one of
them featured on the front page of HN. And I've good stats on kaggle also. I'm
applying for a job in top ML firm. I'm fresh grad. Should I apply for Software
engineer or research engineer/Data scientist? Is my experience enough for
research engineer/Data scientist?

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hiddencost
Anecdotally, a lot of the people taking these classes totally lack the
background or intuition needed and aren't getting any real training in machine
learning. They're learning some very rudimentary bits of data cleaning and how
to use basic machine learning libraries.

I recently interviewed someone taking a (reputable) online masters in machine
learning, and they couldn't describe how or why any of the models they were
using worked, nor could they answer most basic questions about the problems /
data they were working on.

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shas3
This could be a real problem in their respective careers (or not). Here's the
analogy: scientists who use statistical tools as blackboxes are the ones
responsible for the whole problem with misusing p-values. Similarly, poor
intuition and training in machine learning leads to the blackbox mentality and
consequently, problems with building working systems.

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forgetsusername
That's not really an analogy, it's an assertion without a shred of evidence.

I'm torn about the blackbox thing. On one hand, it's important to understand
the underpinnings of a model. On the other, we utilize a _multitude_ of things
in our daily lives of which we have no fundamental understanding; that's
abstraction in a nutshell.

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hiddencost
Machine learning gets kinda scary, though. For one example, discrimination
with ML is super easy. Check out fatml.org for instance. Also, with ML it's
really easyfor an amateur to over fit like crazy and draw spurious conclusions
due to poor methods. People think they have an intelligence when they instead
have very finicky tools

Edit: another pointer here
[https://algorithmicfairness.wordpress.com](https://algorithmicfairness.wordpress.com)

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cageface
This is fascinating stuff and well worth learning for its own sake but I think
people that are jumping on this bandwagon with the idea that there is a
bonanza of high paying jobs waiting for them are going to be disappointed.
Even very heavily data-centric companies only hire a few ML specialists for
every handful of general purpose code monkeys.

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myth_drannon
100% true, most of time companies hire PhDs only.

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nowey
Ooyala has only 3 data scientists. All have PhDs and come from a
math/statistics/com sci background. Kueski only has 1!

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manyxcxi
I'm currently enrolled in a Stanford ML class on Coursera. I'm not very far
into it so I don't have a lot to say about the class in particular but I know
WHY I signed up for it.

I graduated from uni with a Computer Engineering degree in 2005- ML wasn't
really a thing being offered at that point. I loved algebra and calculus but
hated statistics. All I hear about these days is machine learning, so I wanted
to see what the fuss was all about, as I've really never been exposed to it. I
also wanted a university style exposure to it as I wanted to ease into some of
the statistical concepts necessary as I wasn't good at them way back when and
I haven't practiced them in 10 years.

Finally, after some initial exposure, I may find that ML or some of its
concepts will be another tool in my problem solving tool bag.

I feel like a lot of people have my same motivation. We are hearing all the
hype, we weren't really exposed in our formative years, and we're curious.
Also, my particular course was free. So what's the harm?

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GrantS
Good points. Just to clarify in case folks take your experience in 2005 as the
rule, while certainly not as widespread as today, ML was definitely being
offered at various universities in the late 90s and early 2000s: for example,
the textbook "Machine Learning" by Tom Mitchell [1] was published in 1997 and
was used in the undergrad ML class at Georgia Tech when I took it in
2001-2002.

[1]
[http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlboo...](http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html)

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manyxcxi
That was why I said wasn't really a thing being offered. There were certainly
some classes that dabbled in the general area, but nothing that I can remember
that came out and said "this is Machine Learning".

At least in my Computer Engineering curriculum it was very much about the
electrical engineering and software development fundamentals.

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narrator
I've read that machine learning is making statistics classes fall out of
favor. I can understand why. In a lot of ways, machine learning is statistics
in reverse. Instead of starting with a hypothesis and trying to reject the
null hypothesis, unsupervised machine learning starts with data and generates
hypothesis.

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arcanus
It's more than just statistics, broadly speaking that is the scientific
process.

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vonmoltke
It is _part of_ the scientific process, just as it is part of the machine
learning process. The hypothesis has to come from somewhere. In classic
science, it comes from a scientist or group of scientists who notice something
strange or see a correlation, devise an explanation, then test it to see of it
is correct. Unsupervised machine learning just automates the second and part
of the first step by having one or more algorithms devise the hypotheses
instead of humans.

ML is still _supposed_ to do the third step. IMO, where ML often falls down
due to the immaturity of the field is in not creating good experiments to test
the models (hypotheses) generated by the algorithms.

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pwm
Side note: Zen and the Art of Motorcycle Maintenance is a classic read about
"The hypothesis has to come from somewhere".

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pmarreck
_Concretely,_ does anyone know of a good competitor to Andrew Ng's infamous
Coursera class? His material dates to 2011 and that's an eternity in the
computing world

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Cacti
ML is almost entirely based upon math that's decades upon decades old. The
only reason it's become such a big thing lately is that the hardware and data
sets have finally caught up to the point where it's useful on a broad scale.

Sure, the research portion of the field has made a lot of strides since 2011
but for anything that's not PhD or research level stuff, Ng's class is
perfectly up to date.

You'd be hard pressed to find a better class anywhere.

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askafriend
I took an Intro AI class in college (a broad ML class with some other
techniques thrown in) and to be honest, I found it incredibly boring. I don't
mean this to be a dig at the field, but I just thought I'd share my personal
feelings. I didn't much enjoy the process that ML required but the end result
was fairly cool (though not personally rewarding enough to be worth the
process).

Distributed Systems was far more fascinating in my opinion.

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sundarurfriend
This has generally been my experience too, the results of AI are cool and
exciting, but the programming side often ends up dull and boring - accentuated
by the "I have no idea why tuning that parameter worked" factor that happens
in any complex AI system.

Introducing senses and movement - i.e., robotics - makes the boring parts
worth it for me though.

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mud_dauber
If you have a background in hardware design / chip architecture, things get
even more interesting. Figuring out how to balance memory bandwidth, power and
optimal datatypes (think integer v. floating point) is fascinating stuff.

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Radim
This is great news; with the CS & ML basics covered, we in the industry [1]
are only left with instilling some common sense into the graduates :)

In academia, it's publish or perish, so much of the cutting edge research is
over-engineered (over-researched?) and too brittle to be relied on in
production. Not to mention lacking a usable implementation.

Because in practice, many business problems that call for automation and ML
can be solved using the simplest of techniques to a satisfactory degree. The
challenge fresh graduates face is rarely advanced math. It's usually _solving
the right problem_ and _making the solution robust enough to be reliable_ in
production (+communicating this to all stakeholders).

Model interpretability and your ability to analyse errors and iterate the
solution are worth way more than a few percent gain in accuracy (accuracy/f1
are rarely the measures most relevant to the business goals too; the cost
matrix is usually trickier than that). Pulling every opaque deep learning
library under the sun into a system that could be solved using a few regexps
and ifs to get a 5% KPI boost is not a good idea.

Building practical Machine Learning models is as much about solid engineering
and understanding the business objectives as it is about math&theory, though
the math cannot be skipped. We're not nearly at the stage where some "generic
ML in the cloud" can cut it, wherever there's real money on the line.
Successful systems are still very domain specific, built with significant SME
expertise.

[1] Source & plug: we run a ML mentoring program for promising university
students, as well as give corporate trainings on Machine Learning in Python:
[http://rare-technologies.com](http://rare-technologies.com)

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jchendy
This is definitely consistent with my experience. Every candidate I've
interviewed for software engineering internships this year says that their
main technical interest is machine learning.

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goldfeld
Meaning no offense to you, sounds like a terribly boring bunch. More
constructively, it seems from present time like putting oneself on a fast
track to become commodity.

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sonabinu
Strengthening your comfort with math and algorithms is the first step. If you
are doing your undergrad, do as many math classes as you possibly can. I think
the hardest part of breaking into a career in machine learning and data
science is learning the math on your own. Being enrolled in a real class
helps.

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woodcut
In laymans terms what's the intersection between ML and AI?

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kcanini
ML is a subset of AI.

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zodiac
So your answer should have been "ML" :p

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wslh
Genuine question: how do you benefit from this classes if you just want to use
third-party libraries? I mean, you can now do amazing things just using
others' APIs without knowing what's behind.

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pmarreck
There seems to be a bit of an art to choosing your machine learning algorithm
and parameters. The class is a good way to get a grasp on that.

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danvoell
I agree. Also, I think there are going to be a proliferation of API's to
choose from and it will be helpful to understand which ones are the best and
why. Some could be flat out wrong.

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AKluge
Another useful, related, online course, Statistical Aspects of Data Mining
(Stats 202) from Stanford & GoogleTechTalks
[https://www.youtube.com/watch?v=zRsMEl6PHhM&list=PLA4B3B4CB6...](https://www.youtube.com/watch?v=zRsMEl6PHhM&list=PLA4B3B4CB6949A800)

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joaoaccarvalho
I've studied EE before at master's level, but I'm going back to do a master in
CS with 4/5 courses in ML. In the end I feel there is a great advantage in
being able to implement the algorithms you design for ML applications. What do
you think?

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Cacti
The ML class here is the most popular class by far in the entire department,
enough that it's the only class where enrollment is based upon an exam. Half
of the "enrolled" ended up either getting cut or dropping before the first day
of class.

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bronz
For people who want to learn more about machine learning, check out this free
textbook written by Yoshua Bengio.

[http://www.deeplearningbook.org/](http://www.deeplearningbook.org/)

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jonesb6
I always felt classes in emerging fields were a little premature. It seems
like it always takes a few years for professors to learn how to teach it, much
less what exactly they should be teaching.

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brianchu
Yep. Berkeley only recently started offering a ML class in 2013. The last time
it was offered was 1988-1990, the last AI boom (assuming the course numbers
meant the same thing back then).

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phantomlord
tagging this thread for future reference

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zump
saved

