The Three Minds of AI: Understanding Different types of Machine Learning

We're living in the era of AI, where tech giants constantly unveil new, complex terms, leaving us scrambling to expand our AI vocabulary. But before diving into the latest buzzwords, it's essential to grasp the fundamentals that underpin this technological revolution. So, what exactly is machine learning?

Essentially, machine learning empowers computers to learn and perform tasks based on data. We feed the computer vast amounts of data (the 'training data'), and sophisticated algorithms help it identify patterns. When we ask questions, the computer can then provide answers based on the knowledge it gained from this training.

A more formal definition of Machine Learning is:

Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. - Arthur Samuel

The crucial element here is data. Its quality significantly impacts the machine's learning. The entire machine learning field can be broadly categorized into three main types based on how different categories of training data influence the machine learning:

2. Supervised Learning

3. Unsupervised Learning

4. Reinforcement Learning

Let's look into these one by one.

Supervised Learning: Learning with a Teacher

Remember when we were in school, and our math textbooks had theory, then some solved examples, and then lots of exercise questions whose answers were written on the last pages of the book (thankfully, they were written at least!). The idea was simple: you learn a new concept and some formulas, then go through solved examples to get familiar with how to solve these problems, and eventually, you solve most of the exercise questions, setting aside some difficult ones—outliers are everywhere.

What if I told you this idea itself is supervised learning?

Supervised in the sense that you were given both questions and their correct answers during your training phase, and once you were confident in solving such questions, the teacher threw more questions at you, but this time with no answers, and you were expected to solve these questions with good accuracy.

Machines are no different when it comes to learning. So, in a supervised learning environment, our training data involves 'labeled' data. This means each data point comes with a corresponding 'answer' or 'label.'

We provide the machine with both the problem and the solution (the label). The machine learns patterns (or technically speaking, parameters) by observing these labels.

After training, the machine can accurately answer new questions. Of course, just like achieving 100% in math requires extensive practice, machines also need substantial training to reach high accuracy.

Real-World Examples:

• Predicting housing prices based on historical sales data.

• Detecting spam emails.

• Forecasting sales based on past sales records.

• Image recognition where each image is labeled.

Much of the current AI development is focused on this area of Supervised Learning. One of the challenges in this area is the availability of labeled data.

Unsupervised Learning: Discovering Hidden Patterns

Now that we understand supervised learning and labeled data, unsupervised learning is easier to grasp. Here, the data is 'unlabeled.' The machine must discover patterns and make decisions without explicit guidance.

Let's say we go on a trek to the Himalayan ranges, and during this beautiful trek, we find lots of different plants. Although we cannot tell their names (labels) correctly, at least by visual appearance, we can group them into different clusters based on some distinguishing attributes like the height of the plant or the color of the flower, etc.

In this case, we do not have correct labels, i.e., names of plants. But still, we are able to do some learning, some insights about the plants, which is also useful.

This type of learning is called unsupervised learning, where the machine tries to detect some pattern from the data.

Example:

• Market basket analysis, where retailers find out which products are frequently bought together.

• News clustering based on common news topics say sports, education, health etc.

• Anomaly detection, finding unusual data points.

Unsupervised learning is a promising field with significant potential for future development.

Reinforcement Learning: Learning Through Trial and Error

Reinforcement learning involves an 'agent' interacting with an 'environment.' The agent learns by receiving 'rewards' for desired actions and 'penalties' for undesirable ones.

Think of learning to ride a bicycle. You try different balancing techniques, and when you stay upright, you feel a sense of 'reward' (success). When you fall, you experience a 'penalty' (failure). Over time, you refine your technique to maximize the 'reward' of staying balanced.

Real-World Examples:

• Autonomous driving systems.

• Robotics in industrial automation.

The agent aims to maximize its cumulative rewards over time. This approach is powerful for tasks that involve sequential decision-making.

In essence:

• Supervised Learning relies on labeled data.

• Unsupervised Learning discovers patterns within unlabeled data.

• Reinforcement Learning learns through feedback.

Understanding these fundamental types of machine learning is crucial for navigating the ever-evolving world of AI. By focusing on these core concepts, we can better understand and appreciate the incredible advancements happening around us.