What is Machine Learning?
Hello there, fellow Machine Learning (ML) students! Welcome back to our crash course in starting machine learning from an absolute beginner’s perspective. In our previous article, we covered an introduction to Machine Learning, answering several key questions: Where is machine learning used in our lives? Where did machine learning come from? Where is machine learning […]
Hello there, fellow Machine Learning (ML) students! Welcome back to our crash course in starting machine learning from an absolute beginner’s perspective.
In our previous article, we covered an introduction to Machine Learning, answering several key questions:
- Where is machine learning used in our lives?
- Where did machine learning come from?
- Where is machine learning headed?
Forging ahead in our learning journey, we’ll introduce some definitions of machine learning and look at the major types of machine learning applications.
Machine Learning (ML), A Casual Definition by Arthur Samuel
Our first definition, teased in the last article, follows:
Machine learning is the practice of giving computers the ability to learn without being explicitly programmed to do so.
More on Arthur Samuel & Why His Definition on ML Matters
If you’re like me, you might not have heard of Arthur Samuel. Who is he, and why does his opinion matter in the fields of artificial intelligence and machine learning?
Arthur Samuel was a pioneer in artificial intelligence and computer gaming fields. In 1959, he coined the term “machine learning” as a founding father in the field. That’s why he’s important! Let’s also look at a more formal / scientific definition.
A More Formal Machine Learning Definition
Tom Mitchell, of Carnegie Mellon, offers a definition with more structure.
- A well defined learning problem follows
- E * T = P
- Note: His definition does not include mathematical operators. I’m taking a large liberty to insert them myself. ¯\_(ツ)_/¯
- Experience (E) placed against Task (T) is measured by Performance (P)
Here’s a further example:
Example: playing Go.
E = the experience of playing many games of Go.
T = the task of playing and winning Go.
P = the probability that the program will win the next game.
Major Categories of Machine Learning Algorithms
If you judge by press coverage of ML as I have, it appears to be a nebulous field. (In all fairness, it may still be.) However, there is structure we can take in learning ML. There are two types of machine learning algorithms:
- Supervised learning algorithms
- Unsupervised learning algorithms
There are a couple of other prominent types of machine learning algorithms as well: reinforcement learning and recommender systems.
Wrap-Up
Congratulations, we’ve cleared a very gentle introduction to machine learning, and it’s novice/high level definitions. I look forward to learning more with you, dear reader! Our next articles will cover a bit more detail about the two major ML algorithm types: supervised learning, and unsupervised learning. Until then, look after each other.
