Practical AI/Machine Learning discussion by building a Recommendation System using Collaborative Filtering via Matrix Factorization.

1. Practical AI - Building a
Recommendation System
John Paul Ada

2. Artificial Intelligence

3. Machines built for
simulating human
intelligence.

4. Machine Learning

5. Learning without
being told how to.

6. ML Problems
Here are some types of problems
Machine Learning is trying to
solve.
● Regression
● Classification
● Clustering
● Recommendation

7. ML Categories
Here are some of the current
Machine Learning categories.
● Supervised Learning
● Unsupervised Learning
● Semi-Supervised Learning
● Reinforcement Learning

8. Recommendation

9. Content-based
Recommendations

10. Recommending
something based
on its features.

11. Collaborative Filtering

12. Recommending
based on past
customer ratings.

13. Matrix Factorization

14. Finding smaller
matrices whose
product would result
in the completed
target matrix.

15. Latent Factors

16. The features of
the object that
you don’t know
about.

17. Matrix Factorization
1. Create two matrices with random
values. Each matrix should have the
same number of rows as there are
users and as many columns as your
preferred number of latent factors.
2. Adjust the two matrices until their
product nearly matches the target
matrix (gradient descent).
3. Multiply the matrices to get the
completed matrix.

18. Factor Matrices

19. Completed Matrix

20. Questions?

21. Exercise