Supervised vs. Unsupervised Learning: Algorithms, Examples & Differences
The human brain is among the most complicated organs and one of nature’s most amazing creations. The brain’s capacity is considered limitless; there isn’t a thing it can’t remember. Although many often don’t think about it, the processes that happen in the mind are fascinating.
As technology evolved over the years, scientists figured out a way to make machines think like humans, and this process is called machine learning. Like cars need fuel to operate, machines need data and algorithms. With the application of adequate techniques, machines can learn from this data and even improve their accuracy as time passes.
Two basic machine learning approaches are supervised and unsupervised learning. You can already assume the biggest difference between them based on their names. With supervised learning, you have a “teacher” who shows the machine how to analyze specific data. Unsupervised learning is completely independent, meaning there are no teachers or guides.
This article will talk more about supervised and unsupervised learning, outline their differences, and introduce examples.
Imagine a teacher trying to teach their young students to write the letter “A.” The teacher will first set an example by writing the letter on the board, and the students will follow. After some time, the students will be able to write the letter without assistance.
Supervised machine learning is very similar to this situation. In this case, you (the teacher) train the machine using labeled data. Such data already contains the right answer to a particular situation. The machine then uses this training data to learn a pattern and applies it to all new datasets.
Note that the role of a teacher is essential. The provided labeled datasets are the foundation of the machine’s learning process. If you withhold these datasets or don’t label them correctly, you won’t get any (relevant) results.
Supervised learning is complex, but we can understand it through a simple real-life example.
Suppose you have a basket filled with red apples, strawberries, and pears and want to train a machine to identify these fruits. You’ll teach the machine the basic characteristics of each fruit found in the basket, focusing on the color, size, shape, and other relevant features. If you introduce a “new” strawberry to the basket, the machine will analyze its appearance and label it as “strawberry” based on the knowledge it acquired during training.
Types of Supervised Learning
You can divide supervised learning into two types:
- Classification – You can train machines to classify data into categories based on different characteristics. The fruit basket example is the perfect representation of this scenario.
- Regression – You can train machines to use specific data to make future predictions and identify trends.
Supervised Learning Algorithms
Supervised learning uses different algorithms to function:
- Linear regression – It identifies a linear relationship between an independent and a dependent variable.
- Logistic regression – It typically predicts binary outcomes (yes/no, true/false) and is important for classification purposes.
- Support vector machines – They use high-dimensional features to map data that can’t be separated by a linear line.
- Decision trees – They predict outcomes and classify data using tree-like structures.
- Random forests – They analyze several decision trees to come up with a unique prediction/result.
- Neural networks – They process data in a unique way, very similar to the human brain.
Supervised Learning: Examples and Applications
There’s no better way to understand supervised learning than through examples. Let’s dive into the real estate world.
Suppose you’re a real estate agent and need to predict the prices of different properties in your city. The first thing you’ll need to do is feed your machine existing data about available houses in the area. Factors like square footage, amenities, a backyard/garden, the number of rooms, and available furniture, are all relevant factors. Then, you need to “teach” the machine the prices of different properties. The more, the better.
A large dataset will help your machine pick up on seemingly minor but significant trends affecting the price. Once your machine processes this data and you introduce a new property to it, it will be able to cross-reference its features with the existing database and come up with an accurate price prediction.
The applications of supervised learning are vast. Here are the most popular ones:
- Sales – Predicting customers’ purchasing behavior and trends
- Finance – Predicting stock market fluctuations, price changes, expenses, etc.
- Healthcare – Predicting risk of diseases and infections, surgery outcomes, necessary medications, etc.
- Weather forecasts – Predicting temperature, humidity, atmospheric pressure, wind speed, etc.
- Face recognition – Identifying people in photos
Imagine a family with a baby and a dog. The dog lives inside the house, so the baby is used to it and expresses positive emotions toward it. A month later, a friend comes to visit, and they bring their dog. The baby hasn’t seen the dog before, but she starts smiling as soon as she sees it.
Because the baby was able to draw her own conclusions based on the new dog’s appearance: two ears, tail, nose, tongue sticking out, and maybe even a specific noise (barking). Since the baby has positive emotions toward the house dog, she also reacts positively to a new, unknown dog.
This is a real-life example of unsupervised learning. Nobody taught the baby about dogs, but she still managed to make accurate conclusions.
With supervised machine learning, you have a teacher who trains the machine. This isn’t the case with unsupervised learning. Here, it’s necessary to give the machine freedom to explore and discover information. Therefore, this machine learning approach deals with unlabeled data.
Types of Unsupervised Learning
There are two types of unsupervised learning:
- Clustering – Grouping uncategorized data based on their common features.
- Dimensionality reduction – Reducing the number of variables, features, or columns to capture the essence of the available information.
Unsupervised Learning Algorithms
Unsupervised learning relies on these algorithms:
- K-means clustering – It identifies similar features and groups them into clusters.
- Hierarchical clustering – It identifies similarities and differences between data and groups them hierarchically.
- Principal component analysis (PCA) – It reduces data dimensionality while boosting interpretability.
- Independent component analysis (ICA) – It separates independent sources from mixed signals.
- T-distributed stochastic neighbor embedding (t-SNE) – It explores and visualizes high-dimensional data.
Unsupervised Learning: Examples and Applications
Let’s see how unsupervised learning is used in customer segmentation.
Suppose you work for a company that wants to learn more about its customers to build more effective marketing campaigns and sell more products. You can use unsupervised machine learning to analyze characteristics like gender, age, education, location, and income. This approach is able to discover who purchases your products more often. After getting the results, you can come up with strategies to push the product more.
Unsupervised learning is often used in the same industries as supervised learning but with different purposes. For example, both approaches are used in sales. Supervised learning can accurately predict prices relying on past data. On the other hand, unsupervised learning analyzes the customers’ behaviors. The combination of the two approaches results in a quality marketing strategy that can attract more buyers and boost sales.
Another example is traffic. Supervised learning can provide an ETA to a destination, while unsupervised learning digs a bit deeper and often looks at the bigger picture. It can analyze a specific area to pinpoint accident-prone locations.
Differences Between Supervised and Unsupervised Learning
These are the crucial differences between the two machine learning approaches:
- Data labeling – Supervised learning uses labeled datasets, while unsupervised learning uses unlabeled, “raw” data. In other words, the former requires training, while the latter works independently to discover information.
- Algorithm complexity – Unsupervised learning requires more complex algorithms and powerful tools that can handle vast amounts of data. This is both a drawback and an advantage. Since it operates on complex algorithms, it’s capable of handling larger, more complicated datasets, which isn’t a characteristic of supervised learning.
- Use cases and applications – The two approaches can be used in the same industries but with different purposes. For example, supervised learning is used in predicting prices, while unsupervised learning is used in detecting customers’ behavior or anomalies.
- Evaluation metrics – Supervised learning tends to be more accurate (at least for now). Machines still require a bit of our input to display accurate results.
Do you need to teach your machine different data, or can you trust it to handle the analysis on its own? Think about what you want to analyze. Unsupervised and supervised learning may sound similar, but they have different uses. Choosing an inadequate approach leads to unreliable, irrelevant results.
Supervised learning is still more popular than unsupervised learning because it offers more accurate results. However, this approach can’t handle larger, complex datasets and requires human intervention, which isn’t the case with unsupervised learning. Therefore, we may see a rise in the popularity of the unsupervised approach, especially as the technology evolves and enables more accuracy.
Soon, we will be launching four new Degrees for AY24-25 at OPIT – Open Institute of Technology
I want to offer a behind-the-scenes look at the Product Definition process that has shaped these upcoming programs.
🚀 Phase 1: Discovery (Late May – End of July)
Our journey began with intensive brainstorming sessions with OPIT’s Academic Board (Francesco Profumo, Lorenzo Livi, Alexiei Dingli, Andrea Pescino, Rosario Maccarrone) . We also conducted 50+ interviews with tech and digital entrepreneurs (both from startups and established firms), academics and students. Finally, we deep-dived into the “Future of Jobs 2023” report by the World Economic Forum and other valuable research.
🔍 Phase 2: Selection – Crafting Our Roadmap (July – August)
Our focus? Introducing new degrees addressing critical workforce shortages and upskilling/reskilling needs for the next 5-10 years, promising significant societal impact and a broad market reach.
Our decision? To channel our energies on full BScs and MScs, and steer away from shorter courses or corporate-focused offerings. This aligns perfectly with our core mission.
💡 Focus Areas Unveiled!
We’re thrilled to concentrate on pivotal fields like:
- Advanced AI
- Digital Business
- Metaverse & Gaming
- Cloud Computing (less “glamorous”, but market demand is undeniable).
🎓 Phase 3: Definition – Shaping the Degrees (August – November)
With an expert in each of the above fields, and with the strong collaboration of our Academic Director, Prof. Lorenzo Livi , we embarked on a rigorous “drill-down process”. Our goal? To meld modern theoretical knowledge with cutting-edge competencies and skills. This phase included interviewing over 60+ top academics, industry professionals, and students and get valuable, program-specific, insights from our Marketing department.
🌟 Phase 4: Accreditation and Launch – The Final Stretch
We’re currently in the accreditation process, gearing up for the launch. The focus is now shifting towards marketing, working closely with Greta Maiocchi and her Marketing and Admissions team. Together, we’re translating our new academic offering into a compelling value proposition for the market.
Stay tuned for more updates!
Far from being a temporary educational measure that came into its own during the pandemic, online education is providing students from all over the world with new ways to learn. That’s proven by statistics from Oxford Learning College, which point out that over 100 million students are now enrolled in some form of online course.
The demand for these types of courses clearly exists.
In fact, the same organization indicates that educational facilities that introduce online learning see a 42% increase in income – on average – suggesting that the demand is there.
Enter the Open Institute of Technology (OPIT).
Delivering three online courses – a Bachelor’s degree in computer science and two Master’s degrees – with more to come, OPIT is positioning itself as a leader in the online education space. But why is that? After all, many institutions are making the jump to e-learning, so what separates OPIT from the pack?
Here, you’ll discover the answers as you delve into the five reasons why you should trust OPIT for your online education.
Reason 1 – A Practical Approach
OPIT focuses on computer science education – a field in which theory often dominates the educational landscape. The organization’s Rector, Professor Francesco Profumo, makes this clear in a press release from June 2023. He points to a misalignment between what educators are teaching computer science students and what the labor market actually needs from those students as a key problem.
“The starting point is the awareness of the misalignment,” he says when talking about how OPIT structures its online courses. “That so-called mismatch is generated by too much theory and too little practical approach.” In other words, students in many classes spend far too much time learning the “hows” and “whys” behind computerized systems without actually getting their hands dirty with real work that gives them practical experience in using those systems.
OPIT takes a different approach.
It has developed a didactic approach that focuses far more on the practical element than other courses. That approach is delivered through a combination of classroom sessions – such as live lessons and masterclasses – and practical work offered through quizzes and exercises that mimic real-world situations.
An OPIT student doesn’t simply learn how computers work. They put their skills into practice through direct programming and application, equipping them with skills that are extremely attractive to major employers in the tech field and beyond.
Reason 2 – Flexibility Combined With Support
Flexibility in how you study is one of the main benefits of any online course.
You control when you learn and how you do it, creating an environment that’s beneficial to your education rather than being forced into a classroom setting with which you may not feel comfortable. This is hardly new ground. Any online educational platform can claim that it offers “flexibility” simply because it provides courses via the web.
Where OPIT differs is that it combines that flexibility with unparalleled support bolstered by the experiences of teachers employed from all over the world. The founder and director of OPIT, Riccardo Ocleppo, sheds more light on this difference in approach when he says, “We believe that education, even if it takes place physically at a distance, must guarantee closeness on all other aspects.” That closeness starts with the support offered to students throughout their entire study period.
Tutors are accessible to students at all times. Plus, every participant benefits from weekly professor interactions, ensuring they aren’t left feeling stuck on an educational “island” and have to rely solely on themselves for their education. OPIT further counters the potential isolation that comes with online learning with a Student Support team to guide students through any difficulties they may have with their courses.
In this focus on support, OPIT showcases one of its main differences from other online platforms.
You don’t simply receive course material before being told to “get on with it.” You have the flexibility to learn at your own pace while also having a support structure that serves as a foundation for that learning.
Reason 3 – OPIT Can Adapt to Change Quickly
The field of computer science is constantly evolving.
In the 2020s alone, we’ve seen the rise of generative AI – spurred on by the explosive success of services like ChatGPT – and how those new technologies have changed the way that people use computers.
Riccardo Ocleppo has seen the impact that these constant evolutions have had on students. Before founding OPIT, he was an entrepreneur who received first-hand experience of the fact that many traditional educational institutions struggle to adapt to change.
“Traditional educational institutions are very slow to adapt to this wave of new technologies and trends within the educational sector,” he says. He points to computer science as a particular issue, highlighting the example of a board in Italy of which he is a member. That board – packed with some of the country’s most prestigious tech universities – spent three years eventually deciding to add just two modules on new and emerging technologies to their study programs.
That left Ocleppo feeling frustrated.
When he founded OPIT, he did so intending to make it an adaptable institution in which courses were informed by what the industry needs. Every member of its faculty is not only a superb teacher but also somebody with experience working in industry. Speaking of industry, OPIT collaborates with major companies in the tech field to ensure its courses deliver the skills that those organizations expect from new candidates.
This confronts frustration on both sides. For companies, an OPIT graduate is one for which they don’t need to bridge a “skill gap” between what they’ve learned and what the company needs. For you, as a student, it means that you’re developing skills that make you a more desirable prospect once you have your degree.
Reason 4 – OPIT Delivers Tier One Education
Despite their popularity, online courses can still carry a stigma of not being “legitimate” in the face of more traditional degrees. Ocleppo is acutely aware of this fact, which is why he’s quick to point out that OPIT always aims to deliver a Tier One education in the computer science field.
“That means putting together the best professors who create superb learning material, all brought together with a teaching methodology that leverages the advancements made in online teaching,” he says.
OPIT’s degrees are all accredited by the European Union to support this approach, ensuring they carry as much weight as any other European degree. It’s accredited by both the European Qualification Framework (EQF) and the Malta Qualification Framework (MQF), with all of its courses having full legal value throughout Europe.
It’s also here where we see OPIT’s approach to practicality come into play via its course structuring.
Take its Bachelor’s degree in computer science as an example.
Yes, that course starts with a focus on theoretical and foundational knowledge. Building a computer and understanding how the device processes instructions is vital information from a programming perspective. But once those foundations are in place, OPIT delivers on its promises of covering the most current topics in the field.
Machine learning, cloud computing, data science, artificial intelligence, and cybersecurity – all valuable to employers – are taught at the undergraduate level. Students benefit from a broader approach to computer science than most institutions are capable of, rather than bogging them down in theory that serves little practical purpose.
Reason 5 – The Learning Experience
Let’s wrap up by honing in on what it’s actually like for students to learn with OPIT.
After all, as Ocleppo points out, one of the main challenges with online education is that students rarely have defined checkpoints to follow. They can start feeling lost in the process, confronted with a metaphorical ocean of information they need to learn, all in service of one big exam at the end.
Alternatively, some students may feel the temptation to not work through the materials thoroughly, focusing instead on passing a final exam. The result is that those students may pass, but they do so without a full grasp of what they’ve learned – a nightmare for employers who already have skill gaps to handle.
OPIT confronts both challenges by focusing on a continuous learning methodology. Assessments – primarily practical – take place throughout the course, serving as much-needed checkpoints for evaluating progress. When combined with the previously mentioned support that OPIT offers, this approach has led to courses that are created from scratch in service of the student’s actual needs.
Choose OPIT for Your Computer Science Education
At OPIT, the focus lies as much on helping students to achieve their dream careers as it does on teaching them. All courses are built collaboratively. With a dedicated faculty combined with major industry players, such as Google and Microsoft, it delivers materials that bridge the skill gap seen in the computer science field today.
There’s also more to come.
Beyond the three degrees OPIT offers, the institution plans to add more. Game development, data science, and cloud computing, to name a few, will receive dedicated degrees in the coming months, accentuating OPIT’s dedication to adapting to the continuous evolution of the computer science industry. Discover OPIT today – your journey into computing starts with the best online education institution available.