Abstract:
Knot theory is the branch of mathematics that studies the mathematical knots. A knot
is an embedding of a circle in R3. Machine learning is a sub-domain of AI which has
manifested impressive applicability in various scientific domains, and provides different
techniques that could be used to identify relations or patterns in the data. Machine learning
could be used in multiple ways in analyzing and exploring knot theory. In this thesis, we
will discuss how machine learning techniques could be used to find relations in knot theory,
particularly, how it could be used to discover relations in different knot invariants. Briefly,
given a training set {xi, yi}, if a machine learning model is successful in predicting a relation
y = f (x) then this is an indication that such a relationship exists, and then the attribution
techniques can help mathematicians to further explore this relation. This technique has
been developed in [1], and we will deeply explore this techniqu
