Bottom up dynamic programming algorithms require some cleverness.
All of the ones listed can be solved with a top down dynamic programing algorithm. Which just means "write recursive solution, add caching to memoize it".
For some of these, you can get cleverer. For example the coin change problem is better solved with an A* search.
Still, very few programmers will actually need these algorithms. The top thing we need is to recognize when we accidentally wrote a quadratic algorithm. A quick scan of https://accidentallyquadratic.tumblr.com/ shows that even good people on prominent projects make that mistake on a constant basis. So apparently being able to produce an algorithm on the test, doesn't translate to catching an algorithmic mistake in the wild.
For the love of me I still can't consistently solve dynamic programming problems. Because "write a clever brute force solution that can be cached" is so broad that there are tons of variations out there, and a slight twist can bring you out of the loop fast.
Project Euler 18. I tried 3 heuristic approaches, before accepting, that to get the real answer without brute forcing it (because it comes back later in non-brute forcable version anyway), I need to find another way. I came up with an optimal solution, but it is still not dynamic programming, which I would also consider inferior to the bottom up solution I have found.
There is only one efficient solution that can be described as bottom up, and that one is in fact dynamic programming.
A top-down solution in this case is in fact strictly worse. Why? Because while both take similar numbers of operations, in the bottom up solution you can throw away a row once you've processed the one above it. But in a top-down solution, you never know when you might call a particular recursive call again.
This is very common. In a bottom up approach, we often know when we're done with data and can throw it away. This memory savings is the reason why people try to learn a bottom up approach. But it does come with the price of trying to figure out various kinds of "tricks". (That in complicated cases, may not be findable.)
All of the ones listed can be solved with a top down dynamic programing algorithm. Which just means "write recursive solution, add caching to memoize it".
For some of these, you can get cleverer. For example the coin change problem is better solved with an A* search.
Still, very few programmers will actually need these algorithms. The top thing we need is to recognize when we accidentally wrote a quadratic algorithm. A quick scan of https://accidentallyquadratic.tumblr.com/ shows that even good people on prominent projects make that mistake on a constant basis. So apparently being able to produce an algorithm on the test, doesn't translate to catching an algorithmic mistake in the wild.