In this course, we are taught unsupervised learning algorithm, recommendation system, And reinforcement learning.
Like reinforcement, learning is not a part of supervised or unsupervised learning algorithm.
I want to ask that if recommendation system that is collaborative filtering Algorithm Part of unsupervised learning or Is it part of supervised learning or none?
Supervised learning uses labelled data, so I think our collaborative filtering is an example of supervised learning.
found this link of a thread helpfull
it is said there its a part of unsupervised learning.
From what I have learned, you can model a recommender system of collaborative filtering as a supervised learning problem, you treat the task of predicting user-item interactions lets say a user rating to a new movie as a regression or classification problem. Other methods such as clustering can be used to model an unsupervised learning recommender system.
Hello @tarunsaxena1000
Then we simply have different opinions which is normal, because I can also have two different opinions on this myself. 
Since I can’t represent the others in that thread, I will share my own different opinions here:
First of all, my understanding of the difference between supervised and unsupervised is whether it is labelled data or not, period.
Now I consider the data (consumed by the model r^{u, i} = w^u \cdot x^i + b^u) that takes the following form as labelled data:
| user u | item i | rating r (label) |
|---|---|---|
| 1 | 2 | 2.5 |
| 2 | 2 | 3.4 |
In this setting, we do gradient descent to train parameters associated with inputs u and i to predict output r to the best. At the end, we will have, for each user and item, one vector of dimension d_1.
Now, on the other hand, sometimes, people take this problem in another approach such as the factorization approach with SVD which, at the end, can also give us, for each user and item, a vector of dimension d_2. However, in this case, it is a factorization approach that takes the ratings as input without the need for any labels.
Our approach for collaborative filtering in Course 3 Week 2 is the first one (gradient descent) and thus I will consider it as supervised. Here I think we are talking about that the approach is supervised, not whether the problem (a colaborative filtering problem for recommendation here) is supervised.
Two approaches on the same problem → two views on whether it is supervised or not. This is how I see it. What do you think?
Cheers,
Raymond
I am a little shy, because it was a mistake, but as long as I still have @rmwkwok’s support, you can read my paper on this: HarvardX/MovieLens/Balducci-MovieLens.pdf at main · anthonypaulbalducci/HarvardX · GitHub These were my early days.
Oops.
You know what, Anthony, I knew you would reply when I typed SVD in my last reply. 
I knew it!
Thanks for clarifying it for me @rmwkwok , it helps a lot.
On web its was getting really confusing as there were mixed opinions. thanks for clearing this confusion.
You are welcome, @tarunsaxena1000!
-Raymond