A.I. & Machine learning is fascinating.

We’re not only inclined to use it in all data driven problem domains, but we also believe that machine learning is the only solution we have at our disposal. However, this may not always be the case.

Even though it can solve a large variety of problems, there are some, which can also be solved using pure data structures & (mathematical) algorithms, which by the way doesn’t need any training data and provides accurate results all the time.

For example, algorithms like KNN et al. seem to be the obvious choice for any problems related to and manipulation of nearest data points but can also be solved by using spatial triangulation concepts which can be implemented as data structures and some of these are available as part of SciPy spatial library.

Similarly, simple decision tables can also be used instead of supervised decision trees, based on the number of decision points.

This talk is all about understanding the opportunities and constraints with respect to using machine learning as compared to using data structures and algorithms. .

To demonstrate the point, I’ll be using the examples of SciPy spatial data structures as well as decision tables to show a working system which works as good as (if not better) machine learning based systems.

By the end of this talk, you’ll know enough to make an informed decision about your choices with respect to machine learning.

The presentation as well as the codes will be shared via GitHub Page post completion of the session.