Machine Learning
“Machine Learning (ML) is a subset
of Artificial Intelligence (AI) that focuses on building algorithms
capable of learning patterns from data and making predictions or
decisions without being explicitly programmed for each task. Instead of
following hard-coded rules, ML models improve their performance through experience—adapting
as they process more data. At its core, ML involves training a model
on historical data allowing them to predict new, similar data without explicit
programming for each task”. All the models we have seen previously (Linear
Regression, Classification and Clustering) constitute Machine Learning
Techniques. There are 3 main categories of learning algorithms, which we will
discuss shortly:
-
Supervised Learning: a model is trained using labelled
datasets. Input and output variables are provided. Let´s take the case of a
stock price prediction. Multiple inputs are provided based on historical data. Linked
to this data an output is provided. When the machine is provided with new
inputs, it will predict an output. The model is readjusted and trained until
the desired output is obtained.
-
Unsupervised Learning: in this case data is not
labelled, and the output is unknown. The model will find patterns in data and
group them together.
-
Reinforcement Learning: an agent interacts with
the environment and performs an action for is rewarded or penalized depending
on the outcome. It is used in autonomous vehicles or industrial robots (movement
or task execution).
Machine Learning is a subset of
Artificial Intelligence and is the basis of Deep Learning. Unlike a simple mathematical
or statistical algorithm, the concept is that the machines “learns” as it is
trained and provides a better answer the next time. This has given rise to the career
of “AI Trainers”, which are people specialized in training the machine. As we will
see later with the rise of LLMs, a model can be trained to be the best mathematician
in the world. As such, a mathematician will provide a mathematical problem to
the model and expect an answer. If the answer is correct, he will reward the model.
If not, he will explain the right answer to it. Next time the model is prompted
for a solution, instead of being rule-based it will apply “logic” and provide a
better answer, and so on until it gets it right.
We have seen many of it´s
applications in previous articles, but others include: perform internet searches,
recognize human speech, diagnose diseases, or build a self-driving car. How
long until machines learn to outpace humans, or achieve Artificial General
Intelligence? We will take that concept later!
What
is Machine Learning? - GeeksforGeeks
What
is Machine Learning? | IBM
What
Is Machine Learning? Definition, Types, and Examples | Coursera
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