Why Should You Study Machine Learning?

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On the 23rd of last month, our in-house Machine Learning expert, Gunnvant Singh conducted a session for Jigsaw students, where he guided students through the ideal way in which to crack Kaggle. This was followed by a thoroughly illuminating discussion about Machine Learning, led by Medy Agami, an adjunct professor with the University of Chicago.

The session kicked off with the rather simple question – why should one study Machine Learning? Naturally, there are a variety of reasons, the most prominent among these being the simplification and automation of work processes across different industries. Professor Agami expanded on this by explaining how Machine Learning and Artificial Intelligence operate on the principle of knowing what the world looks like.

He illustrated this with a very intriguing example of how people function in different fields of work. For instance, a doctor will ask any new patient questions about their health and physical state, in order to gather relevant data. He will then compare this to what “healthy” looks like, and then accordingly take the requisite actions.


Similarly, he also touched upon how this is not dissimilar to normative analysis and how knowing the desired end product is so important. Raw data needs to be compared with what the final product is meant to be, and then you can begin to identify the relevant steps for how to approach a particular problem. First and foremost, knowing the business problem you are trying to solve is vital to how you would proceed – and the model you use would depend heavily on this.

This tied neatly into the next question asked by an audience member on predictive models in marketing. Professor Agami explained how identifying your target audience is a crucial first step in this process. Knowing what attributes they are looking for in a particular product would help you devise a more productive marketing plan. And while stressing on the importance of using data for all this, Professor Agami also highlighted the importance of assumptions in a process like this, and how they should not be disregarded at any point.

The session wound down with him talking about his own experience at the University of Chicago and what sets it apart from other educational institutes. Apart from its high standing in the global education stakes, especially for economics, what really stood out was his assessment of their process.

“At the University of Chicago, they don’t simply give you the problems and the solutions as they see them,” said Professor Agami. “Instead, you will be given real-life use cases to highlight how the concepts can be understood. After that, you are expected to learn how to devise your own frameworks, and this is something that will stand any student in very good stead, particularly later in their careers.”

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