During my first project in McKinsey in 2011, I served the CEO of a bank regarding his small business strategy. I wanted to run a linear regression on the bank's data but my boss told me: "Don't do it. They don't understand statistics". (We did not use Machine Learning but, 7 years down the road, I still believe we developed the right strategy).
Artificial Intelligence is the most general-purpose technology of our time. New products and processes are being developed thanks to better vision systems, speech recognition technologies or recommendation engines based on Machine Learning. In fact, most recent advances in Artificial Intelligence have been achieved in the area of Machine Learning.
Long before McKinsey, in 2004, I started my career as a mobile software developer. At that time I had to write precise instructions for every step of my code. Developing the voice recognition system of today's phones would have been tedious and error-prone back then. It would have required literally hundreds of thousands of detailed instructions to codify every single step, including identifying phonemes from sound waves, grouping them into phonetic words, looking them up in a phonetic dictionary, codifying the meaning with predefined blocks, identifying an answer to the question through a gigantic predefined semantic decision tree ...
Building Amazon Alexa back in 2004 would have been impossible. And it was impossible: Alexa was released in 2014. Machine Learning is a technological breakthrough which allows developers to write software that can learn how to solve a problem without needing to provide step-by-step instructions to it. It is a revolution.
There are three major types of Machine Learning methodologies:
There are many teaching methods. Different teachers use different approaches. Some prefer to teach theory, others prefer to encourage students to practice. One method I found particularly useful when I was at high school was looking at already solved math problems in order to learn how to solve similar problems.
Supervised learning is a collection of algorithms that do exactly that. They can learn how to solve a problem by looking at a lot of examples of the correct answer to that particular problem. This process is called training the algorithm. The solution to the problem can be either categorical or continuous:
There are many kinds of supervised algorithms but most of them are different variants of decision trees, regressions, support vector machines and neural networks, which include deep-learning.
A decision tree is a flowchart-like structure in which tests on different attributes are made subsequently until final decision is met. Simple decision trees are highly interpretable and are widely used by managers. But when the machine tests for thousands of attributes over hundreds of branches things can become too complex for a manager. Some examples of these more advanced tree-based algorithms are random forest, which improves the accuracy of a simple decision tree by averaging multiple decision trees, o gradient boosting trees which also use multiple decision trees in a sequential way in which each tree is trying to correct the errors of the previous tree.
Regressions are a very popular family of supervised algorithms. Due to their simplicity and numerous applications in the field of economics, they were the core part of the statistics curriculum during my MBA at Chicago Booth.
Regressions try to fit a formula with a certain shape on your data. For example, if you think there is a linear relation between your sales and independent factors such as competition prices, your advertising spend, promotions or even the weather, you can train a linear regression algorithm with historical data for which you already know your sales. The algorithm will find the exact parameters of the linear formula, which will be used to predict future sales. There are linear, quadratic, exponential, logarithmic regressions and many others.
