markov-logic-networks

Markov Logic Networks

• Goal: to learn about Markov Logic networks and underlying literature, theory and basic implementation and intuition.
• Result: to demonstrate the learning by attempting to explain what is learnt in a simpler and easy-to-understand approach with an example implementation.

The repository is for the course project for Statistics course, CU Boulder. If any additional resources were used for specific implementation, it is provided in the respective implementation file. The code provided here is implicitly expected not to be replicated for homeworks, assignments or any other programming work. It is welcome to take inspiration, but it is implicitly expected to cite this resource if used for learning, inspiration purposes. Please refer code of conduct.

Reference materials for the study:

• https://people.eecs.berkeley.edu/~russell/papers/cacm15-oupm.pdf
• https://arxiv.org/pdf/1809.10756.pdf
• https://www.youtube.com/watch?v=TRBBlJHnxaQ
• https://www.youtube.com/watch?v=5lZk0ueviWc
• https://homes.cs.washington.edu/~pedrod/papers/pilp.pdf
• https://link.springer.com/content/pdf/10.1007/s10994-006-5833-1.pdf
• https://web.cs.ucla.edu/~guyvdb/slides/IJCAI16EarlyCareer.pdf

Note:

Thanks to the professor and the MSDS program cohort for allowing me to study this topic by myself, which is also outside of the syllabus and curriculum of the course and program.

I chose this topic as part of my interest and long term study goal towards the subject of formal logic, machine learning and AI since 2017.