Artificial Intelligence vs. Machine Learning vs. Deep Learning

Machine Learning vs Deep Learning

Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.

Machine Learning incorporates “ classical ” algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data . The more data you provide to your algorithm, the better it gets.

The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.

For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.

When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?

At this point, you may ask: How do we minimize the error?

One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.

Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.

In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.

Machine Learning is old…

Machine Learning is a pretty old field and incorporates methods and algorithms that have been around for dozens of years , some of them since as early as the sixties.

Some known methods of classification and prediction are the Naive Bayes Classifier and the Support Vector Machines . In addition to the classification, there are also clustering algorithms such as the well-known K-Means and tree-based clustering . To reduce the dimensionality of data to gain more insights about it’ nature methods such as Principal component analysis and tSNE are used.