About
Table of contents
Overview
We aim to foster curiosity and inculcate a research-based mindset among our peers, which will help them explore and dive into the depths of Machine Learning while imbibing the concepts and implementing them efficiently.
What is a Reversed Classroom?
The concept of a Reversed Classroom aligns perfectly with our objective. A reversed classroom is one where most of the learning happens outside the classroom and the teachers are more like guides, who shows a path and the students are encouraged to explore the length and breadth of the topics discussed in class from the comfort of their homes. This type of learning process encourages the students to develop curiosity and quench it on their own, using resources provided by the teachers or discovered independently.
Course Structure
The course is focused on Machine Learning algorithms only. We begin with the Basics of Mathematics, which includes topics such as Dimensions, Representation of Data through matrices, Mappings and Functions and Calculus: performing differentiation on a vector and how it is different from when differentiation is done on scalars. We then go on to give an idea of a Cost Function and its optimization using the example of Linear Regression. Post these two sessions, we shift our attention to other machine learning algorithms such as Logistic Regression, K-Nearest Neighbours, Support Vector Machines, Decision Trees and Random Forests among others. Our focus would be on giving our peers an intuitive understanding of what goes on behind the scences and the mathematics involved behind these algorithms, before implementing them.
More information on the topics taught is given in the Course Calendar page.