In total it might be a lot, but it can't be indepth.
for example "Error and Noise" is something you can spend a whole year studying. What are you really going to cover in one lecture? You'll just touch on a few things that might be useful at some point, but you're not building a larger encompassing understanding of "Error and Noise".
I've taking classes like this. You learn a few useful tricks, but when shit hits the fan in the real world and your tools are not enough you're left floundering. You can't prove things, you can't develop your own methods because you don't understand the principles they're based on.
This would probably be a cool class to take freshman year so that you can figure out for yourself what you'd like to study.
This is clearly an introduction. You're not going to spend a semester talking about "Error and Noise" in a class like this. I had the opposite impression --- that it covers a relatively small number of topics that are very highly related.
Now, will you come out of this class knowing how to do practical machine learning? Probably not. But these are fundamental concepts that you must know. When your tools are not enough in the real world, you can appeal to these concepts to understand why.
I've taking classes like this. You learn a few useful tricks, but when shit hits the fan in the real world and your tools are not enough you're left floundering. You can't prove things, you can't develop your own methods because you don't understand the principles they're based on.
This would probably be a cool class to take freshman year so that you can figure out for yourself what you'd like to study.