What are the 2 classes of categories to help define a problem While studying about machine learning, I've learnt the importance of defining your problem before getting started trying to model it.

I can see 2 types of problem categorification:


Supervised / unsupervised / reinforcement algorithms
Classification / clustering / regression / ranking


Example definitions found on the net:

First type:


Supervised Algorithms: The training data set has inputs as well as the desired output. During the training session, the model will adjust its variables to map inputs to the corresponding output.
Unsupervised Algorithms: In this category, there is not a target outcome. The algorithms will cluster the data set for different groups.
Reinforcement Algorithms: These algorithms are trained on taking decisions. Therefore based on those decisions, the algorithm will train itself based on the success/error of output. Eventually by experience algorithm will able to give good predictions.


Second type:


Classification: You want an algorithm to answer binary yes-or-no questions (cats or dogs, good or bad, sheep or goats, you get the idea) or you want to make a multiclass classification (grass, trees, or bushes; cats, dogs, or birds etc.) You also need the right answers labeled, so an algorithm can learn from them.
Clustering: You want an algorithm to find the rules of classification and the number of classes. The main difference from classification tasks is that you don’t actually know what the groups and the principles of their division are. For instance, this usually happens when you need to segment your customers and tailor a specific approach to each segment depending on its qualities.
Regression: You want an algorithm to yield some numeric value. For example, if you spend too much time coming up with the right price for your product since it depends on many factors, regression algorithms can aid in estimating this value.
Ranking: Some machine learning algorithms just rank objects by a number of features. Ranking is actively used to recommend movies in video streaming services or show the products that a customer might purchase with a high probability based on his or her previous search and purchase activities.


Do each type of categories have a name ? 
And are these types correlated or independent ?
I never saw a name for these categorifications. However, it seems to categorize by type how target variable exists or what is the type of target variable (in supervised and semi-supervised) or input variable (in unsupervised)


[wrong] 2. I think in data mining we call "Reinforcement Algorithms" semi-supervised.


I think Ranking will be a regression problem. just after defined number you can change them to ranks based on their order.
There is some correlation between the first and second type of categorifications you named, you can see that in this picture:



https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d