The presentation goes straight from from linear regression and classification to computer vision and reinforcement learning.
The practical value of ML/AI is what’s in between and is something that isn’t often discussed between all the hype. ML/AI can be used to build models which work well with nontabular data (e.g. text and images), and can solve such regression/classification problems more cleanly. (and with tools like Keras, they’re as easy to train and deploy as a normal model)
I think slide 12 touches on this. Even in the case of an image we can process it pixel by pixel, but that would be lunacy!
For text great results have been achieved using automatons, but they only work for structured strings and break if you add only a little bit of noise.
I feel like ML should be considered whenever you feel like programming something requires you to deal with many different cases, you have a lot of example data available, and having some false positives / true negatives is not a big problem.
Sorry, where do you see this? I see a lot of slides devoted to the "in-between" of random forests, perceptrons, etc. The jump from supervised to unsupervised to RL also makes sense, since RL is a different learning paradigm from the other two.
I'm as exhausted of the ML hype as anyone else, but I believe this deck tempers expectations.
The practical value of ML/AI is what’s in between and is something that isn’t often discussed between all the hype. ML/AI can be used to build models which work well with nontabular data (e.g. text and images), and can solve such regression/classification problems more cleanly. (and with tools like Keras, they’re as easy to train and deploy as a normal model)