In the contemporary world of learning algorithms, along with the aggregate domains plying Machine Learning the complexity of the models itself is swelling. Thus it is important to approach Machine Learning in a conceptual way and in this talk I will present an informal taxonomy of the Machine Learning algorithms, majorly grouped on the different mathematical abstractions.
I will cover Logical Models with tree based or rule based concepts, Geometric Models including linear and distance based approaches, and Probabilistic Models. I will go over the fundamentals of each type of model and discuss their positives, limitations and use cases along-with a very simple hands-on example. The talk will conclude with some pointers on how to explore the data and choose a model to make the learning more efficient.
This talk aims to introduce different categories of Machine Learning models from mathematical point of view and encourage budding ML enthusiasts to reflect on the implications of each corresponding to their domain. By better understanding these types of models attendees will be empowered to design intelligent solutions.