14 ms·

Enrollment Is Surging in Machine Learning Classes

278 points by Osiris30 11y ago

narrator 11y ago
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.
- arcanus 11y ago
  It's more than just statistics, broadly speaking that is the scientific process.
  - vonmoltke 11y ago
    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.
    - pwm 11y ago
      Side note: Zen and the Art of Motorcycle Maintenance is a classic read about "The hypothesis has to come from somewhere".
- beejiu 11y ago
  I guess there's a difference in perspective if you take your machine learning classes with the Mathematics/Stats department rather than the Computer Science department.
- gbrown 11y ago
  I think Statistics departments have lagged behind in teaching ML techniques as first class citizens of the statistical world, but also that ML folks tend to gloss over or ignore a lot of the benefits formal inference and statistical thinking bring to the table. Traditional statistics is very very good at helping us learn a lot about relatively simple (or carefully and deliberately simplified) processes, and provides a rich background in study design. ML techniques are good at helping us learn a little bit about arbitrarily complicated processes, and apply that knowledge quickly. A modern practitioner in either field should have a working knowledge of both [families of] paradigms.
- Cacti 11y ago
  I'm not even sure it's "in reverse." The supervised portion of ML is almost entirely regular ol statistics.
wslh 11y ago
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.
- beejiu 11y ago
  Somebody has to be the 'others'. Somebody has to know what's behind.
- ant6n 11y ago
  You will get more accurate results that are better motivated if you understand the underlying algorithm. Just like you get better results if you understand your data. Don't rely on a magic box.
  - 0xdeadbeefbabe 11y ago
    > Don't rely on a magic box. That's quite funny given the circumstances. I think the neural network algorithm disagrees, and it really wants you to rely on a magic box.
    - argonaut 11y ago
      Have you tried training deep learning models before? Deep learning requires a ton of hyperparameter and architectural tuning, and there are a bajillion data transformation, mathematical optimization, etc. tricks that people use. The end product is still a magic box, but there's a ton of background knowledge required to train these models well.
- pramodliv1 11y ago
  If you want to use a library to find the shortest path in a graph, then the input is simply the list of nodes and edges and is easy to understand. You can use a third party library to solve the problem without knowing how the algorithm works under the hood. But the arguments to a ML algorithm provided by third-party libraries include various parameters to tune the algorithm and make tradeoffs between accuracy/time etc. Debugging the algorithm for your particular dataset also requires some knowledge of how the algorithm works. But there are use cases where you need not know what's happening behind the scenes. For example, you can use a ML as a Service provider to classify your photos without knowing how their algorithms work.
manyxcxi 11y ago
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?
- GrantS 11y ago
  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/mlbook.html http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlboo...
  - manyxcxi 11y ago
    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.
- deepGem 11y ago
  I graduated in 1999, and that too in India. So imagine my situation :). I finished the Coursera ML class recently (though I had done some ML prior to that) and I really enjoyed the class. You should definitely aim to finish it. The assignments towards the end feel relatively simpler compared to the initial ones.
cageface 11y ago
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.
- myth_drannon 11y ago
  100% true, most of time companies hire PhDs only.
  - pskisf 11y ago
    True from my experience as well. If you're going to invest in a Data Scientist, you want the cachet of having a Phd in that role.
  - 0xdeadbeefbabe 11y ago
    Not startups.
  - nowey 11y ago
    Ooyala has only 3 data scientists. All have PhDs and come from a math/statistics/com sci background. Kueski only has 1!
- deleted 11y ago
  [deleted]
- donretag 11y ago
  I have been working in teams with a big ML component for many years, but I have been working from "the outside". I keep on being promised to work more in depth, but never get a chance to. When job hunting last year, I noticed that at almost every company, the division being developers and those working on ML is almost 100%. Both roles are silo'd. Good luck transitioning from dev to ML if you have already started your career. It was not like this just 4 years ago.
- ElysianEagle 11y ago
woodcut 11y ago
In laymans terms what's the intersection between ML and AI?
- kcanini 11y ago
  ML is a subset of AI.
  - zodiac 11y ago
    So your answer should have been "ML" :p
tfgg 11y ago
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/StatLearning/Winter2016/info https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...
- lindbergh 11y ago
  That's nice to hear! I'm doing my masters thesis on statistical learning and I often think how un-glamour this field now is. No bayesianism, less engineering, but having probabilistic guarantees on your out of sample results as well as sample complexity, no matter the underlying distribution, can be quite beneficial.
  - tfgg 11y ago
    If you have time, I'd also read something like David Mackay's information theory textbook [1] for more of a Bayesian perspective. Interviewers did seem to appreciate having multiple perspectives and interpretations of basic results, though less practical. [1] http://www.inference.phy.cam.ac.uk/mackay/itila/ http://www.inference.phy.cam.ac.uk/mackay/itila/
    - nxzero 11y ago
      Given you're making recommendations on the topic of statistics, exactly how many companies did you talk to reach the recommendations you're providing and what if any bias was there in your job search?
      - tfgg 11y ago
        Good points. I'm coming from a computational physics research background and applied for a few data science positions at startups, a large social network and a private ML research group, so not that many overall, beware of the small sample size. The smaller startups seemed to want more "data engineering" experience. What do you mean by "what if any bias was there in your job search"?
sonabinu 11y ago
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.
pmarreck 11y ago
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
- astazangasta 11y ago
  This math has been around since the early 80s. Don't panic.
- Delmania 11y ago
  If you're speaking tools and frameworks, yes, but core data structures, algorithms, and paradigms change very slowly. Andrew's course focuses on the latter, basic ML techniques that have been used for years and will continue to be used for years to come.
- karterk 11y ago
  All of the concepts are still valid since he covers the fundamentals.
- Cacti 11y ago
  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.
- icebraining 11y ago
  As someone pointed out in an earlier thread, the actual course is more advanced than the Coursera class, and its lectures are also available online: https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 As well as the problem sets: https://see.stanford.edu/Course/CS229 https://see.stanford.edu/Course/CS229 They are not any more recent, though.
- jdminhbg 11y ago
Cacti 11y ago
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.
mud_dauber 11y ago
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.
hiddencost 11y ago
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.
- nowey 11y ago
  You need to know the background which is Statistics Discrete Math Algorithms Most books on the subject assume you already know what linear regression is, Naive Beyes is just explained briefly theoretically and it goes right into the code in spark, R or Clojure for example. However UC Berkeley's course is very theoretical, almost no code is shown and its just MATLAB code (don't remember the name off the top of my head), their spark course though is heavy on the code with IPython activities.
- shas3 11y ago
  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.
  - forgetsusername 11y ago
    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.
    - hiddencost 11y ago
jchendy 11y ago
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.
- goldfeld 11y ago
  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.
askafriend 11y ago
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.
- sundarurfriend 11y ago
  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.
bronz 11y ago
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/
phantomlord 11y ago
tagging this thread for future reference
jonesb6 11y ago
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.
joaoaccarvalho 11y ago
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?
AKluge 11y ago
Another useful, related, online course, Statistical Aspects of Data Mining (Stats 202) from Stanford & GoogleTechTalks https://www.youtube.com/watch?v=zRsMEl6PHhM&list=PLA4B3B4CB6949A800 https://www.youtube.com/watch?v=zRsMEl6PHhM&list=PLA4B3B4CB6...
Radim 11y ago
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
brianchu 11y ago
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).
zump 11y ago
saved

278 points by Osiris30 11y ago

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.

arcanus 11y ago
It's more than just statistics, broadly speaking that is the scientific process.
- vonmoltke 11y ago
  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.
  - pwm 11y ago
    Side note: Zen and the Art of Motorcycle Maintenance is a classic read about "The hypothesis has to come from somewhere".
beejiu 11y ago
I guess there's a difference in perspective if you take your machine learning classes with the Mathematics/Stats department rather than the Computer Science department.
gbrown 11y ago
I think Statistics departments have lagged behind in teaching ML techniques as first class citizens of the statistical world, but also that ML folks tend to gloss over or ignore a lot of the benefits formal inference and statistical thinking bring to the table. Traditional statistics is very very good at helping us learn a lot about relatively simple (or carefully and deliberately simplified) processes, and provides a rich background in study design. ML techniques are good at helping us learn a little bit about arbitrarily complicated processes, and apply that knowledge quickly. A modern practitioner in either field should have a working knowledge of both [families of] paradigms.
Cacti 11y ago
I'm not even sure it's "in reverse." The supervised portion of ML is almost entirely regular ol statistics.

wslh 11y ago

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.

beejiu 11y ago
Somebody has to be the 'others'. Somebody has to know what's behind.
ant6n 11y ago
You will get more accurate results that are better motivated if you understand the underlying algorithm. Just like you get better results if you understand your data. Don't rely on a magic box.
- 0xdeadbeefbabe 11y ago
  > Don't rely on a magic box. That's quite funny given the circumstances. I think the neural network algorithm disagrees, and it really wants you to rely on a magic box.
  - argonaut 11y ago
    Have you tried training deep learning models before? Deep learning requires a ton of hyperparameter and architectural tuning, and there are a bajillion data transformation, mathematical optimization, etc. tricks that people use. The end product is still a magic box, but there's a ton of background knowledge required to train these models well.
pramodliv1 11y ago
If you want to use a library to find the shortest path in a graph, then the input is simply the list of nodes and edges and is easy to understand. You can use a third party library to solve the problem without knowing how the algorithm works under the hood. But the arguments to a ML algorithm provided by third-party libraries include various parameters to tune the algorithm and make tradeoffs between accuracy/time etc. Debugging the algorithm for your particular dataset also requires some knowledge of how the algorithm works. But there are use cases where you need not know what's happening behind the scenes. For example, you can use a ML as a Service provider to classify your photos without knowing how their algorithms work.

manyxcxi 11y ago

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?

GrantS 11y ago
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/mlbook.html http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlboo...
- manyxcxi 11y ago
  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.
deepGem 11y ago
I graduated in 1999, and that too in India. So imagine my situation :). I finished the Coursera ML class recently (though I had done some ML prior to that) and I really enjoyed the class. You should definitely aim to finish it. The assignments towards the end feel relatively simpler compared to the initial ones.

cageface 11y ago

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.

myth_drannon 11y ago
100% true, most of time companies hire PhDs only.
- pskisf 11y ago
  True from my experience as well. If you're going to invest in a Data Scientist, you want the cachet of having a Phd in that role.
- 0xdeadbeefbabe 11y ago
  Not startups.
- nowey 11y ago
  Ooyala has only 3 data scientists. All have PhDs and come from a math/statistics/com sci background. Kueski only has 1!
deleted 11y ago
[deleted]
donretag 11y ago
I have been working in teams with a big ML component for many years, but I have been working from "the outside". I keep on being promised to work more in depth, but never get a chance to. When job hunting last year, I noticed that at almost every company, the division being developers and those working on ML is almost 100%. Both roles are silo'd. Good luck transitioning from dev to ML if you have already started your career. It was not like this just 4 years ago.
ElysianEagle 11y ago

woodcut 11y ago

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

kcanini 11y ago
ML is a subset of AI.
- zodiac 11y ago
  So your answer should have been "ML" :p

tfgg 11y ago

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/StatLearning/Winter2016/info https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...

lindbergh 11y ago
That's nice to hear! I'm doing my masters thesis on statistical learning and I often think how un-glamour this field now is. No bayesianism, less engineering, but having probabilistic guarantees on your out of sample results as well as sample complexity, no matter the underlying distribution, can be quite beneficial.
- tfgg 11y ago
  If you have time, I'd also read something like David Mackay's information theory textbook [1] for more of a Bayesian perspective. Interviewers did seem to appreciate having multiple perspectives and interpretations of basic results, though less practical. [1] http://www.inference.phy.cam.ac.uk/mackay/itila/ http://www.inference.phy.cam.ac.uk/mackay/itila/
  - nxzero 11y ago
    Given you're making recommendations on the topic of statistics, exactly how many companies did you talk to reach the recommendations you're providing and what if any bias was there in your job search?
    - tfgg 11y ago
      Good points. I'm coming from a computational physics research background and applied for a few data science positions at startups, a large social network and a private ML research group, so not that many overall, beware of the small sample size. The smaller startups seemed to want more "data engineering" experience. What do you mean by "what if any bias was there in your job search"?

sonabinu 11y ago

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.

pmarreck 11y ago

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

astazangasta 11y ago
This math has been around since the early 80s. Don't panic.
Delmania 11y ago
If you're speaking tools and frameworks, yes, but core data structures, algorithms, and paradigms change very slowly. Andrew's course focuses on the latter, basic ML techniques that have been used for years and will continue to be used for years to come.
karterk 11y ago
All of the concepts are still valid since he covers the fundamentals.
Cacti 11y ago
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.
icebraining 11y ago
As someone pointed out in an earlier thread, the actual course is more advanced than the Coursera class, and its lectures are also available online: https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 As well as the problem sets: https://see.stanford.edu/Course/CS229 https://see.stanford.edu/Course/CS229 They are not any more recent, though.
jdminhbg 11y ago

Cacti 11y ago

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.

mud_dauber 11y ago

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.

hiddencost 11y ago

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.

nowey 11y ago
You need to know the background which is Statistics Discrete Math Algorithms Most books on the subject assume you already know what linear regression is, Naive Beyes is just explained briefly theoretically and it goes right into the code in spark, R or Clojure for example. However UC Berkeley's course is very theoretical, almost no code is shown and its just MATLAB code (don't remember the name off the top of my head), their spark course though is heavy on the code with IPython activities.
shas3 11y ago
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.
- forgetsusername 11y ago
  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.
  - hiddencost 11y ago

jchendy 11y ago

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.

goldfeld 11y ago
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.

askafriend 11y ago

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.

sundarurfriend 11y ago
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.

bronz 11y ago

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/

phantomlord 11y ago

tagging this thread for future reference

jonesb6 11y ago

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.

joaoaccarvalho 11y ago

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?

AKluge 11y ago

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

Radim 11y ago

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

brianchu 11y ago

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).

zump 11y ago

saved