"Mobile-dev" is more or less application development for a target client, possibly with a client-server relationship. It may incorporate the products of a ML system, or any number of other systems, but is itself the same application development model we've had for 30 years, but with a mobile client.
Machine Learning is applied computational statistics. It and its close cousin "data science" have become incredibly hyped the last few years. I suppose it's a matter of taste, but my view is that applying simple probability to a problem (e.g. "recommending" based on picking the thing most frequently voted up by other users and adjusting the recommendation as data comes in) isn't "machine learning" or "data science", but I've seen it called such by candidates I've interviewed and even one colleague. It's not that simple, and trivializing the terms just contributes to the hype.
Sorry, didn't make it clear, was wondering given all the difference why would one prefer one over the other? Seem like 'in the field' experience is not going to be very much different. Appears to me mobile can be even more fun.
"Mobile-dev" is more or less application development for a target client, possibly with a client-server relationship. It may incorporate the products of a ML system, or any number of other systems, but is itself the same application development model we've had for 30 years, but with a mobile client.
Machine Learning is applied computational statistics. It and its close cousin "data science" have become incredibly hyped the last few years. I suppose it's a matter of taste, but my view is that applying simple probability to a problem (e.g. "recommending" based on picking the thing most frequently voted up by other users and adjusting the recommendation as data comes in) isn't "machine learning" or "data science", but I've seen it called such by candidates I've interviewed and even one colleague. It's not that simple, and trivializing the terms just contributes to the hype.