What is supervised learning and how does it work in AI?

Here are two humorous-but-correct blog posts on this topic: Explaining supervised learning to a kid (or your boss) https://bit.ly/quaesita_slkid What's the difference between classification and regression https://bit.ly/quaesita_classipred

Supervised learning in AI is a method where the model is trained on a labeled dataset, which means it learns from data that already has the answers. Imagine it like a teacher guiding a student through examples. The model looks at the data and makes predictions or decisions. Over time, it adjusts its approach based on the accuracy of its predictions compared to the known outcomes in the training data. This process continues until the model achieves a high level of accuracy. It's commonly used in applications like image recognition, where the model learns from a dataset of images that are already labeled with what's in them (like cats or dogs), and then uses this learning to identify these elements in new, unseen images.

There are two types of supervised learning: regression and classification. Regression predicts continuous quantities like home prices or city temperatures, whereas classification labels inputs like spam detection or image recognition. For both kinds, labelled input-output pairs must be used throughout the training phase. Supervised learning is a strong and scalable framework that advances artificial intelligence and data-driven decision-making in a variety of industries due to its adaptability and capacity to generalise trends.

During supervised learning of either types, the machine uses an algorithm like linear regression, decision tree regression (for regression) or support vector machine, decision trees classifier, neural network etc ( for classification) to learn a mathematical model (an equation) that relates the feature variables to the target variable. These learning algorithms learn the optimal values for the coefficients of feature variables through an optimisation algorithm (e.g., gradient descent), which minimises the loss or errors created by the model.

Here are 2 practical business applications: Classification: Imagine a credit card company using a fraud detection system. This system is a classification model that analyzes transaction data (like amount, location, frequency) and classifies each transaction as either 'fraudulent' or 'legitimate.' The model is trained on historical transaction data where each transaction is already labeled as fraud or non-fraud. Regression: Consider a retail chain predicting future sales. The model is trained on past sales data, considering factors like store location, time of year, marketing efforts, and economic trends. This enables the company to manage inventory, allocate resources, and plan marketing campaigns more effectively.

From my experience there are 6/7 steps and not 4. There is no mention of data collection and preparation in the above mentioned but it is essential. 1/ Data Collection: Gather a set of examples with input data and corresponding correct outputs. 2/ Data preparation: Clean the data to make it suitable for training. 3/Model Selection: Choose a type of model that can learn from the data. 4/Model Training: Show the model the labeled examples and let it learn the patterns. 5/Evaluation: Assess how well the model is performing by using a separate set of data that it hasn't seen during training (unseen data) 6/ Fine-Tuning: asjust parameters 7/ Predictions

Instead of requiring hand specified patterns to calculate outputs, supervised solutions learn patterns from inputs and outputs.

Here's how I would go about doing a typical Supervised Learning project: 1. Collect data: AI is data hungry and without 'representative' examples, your models won't 'learn' anything. 2. Choose model: There are tons of SL models available out there. The choice depends on the size of the dataset, the number of classes and the complexity of the problem. 3. Train and evaluate model: Once a model is chosen, we 'fit' it to our data distribution. The model is then evaluated. Evaluation can be done in multiple ways and depends on the problem at hand. 4. Utilize the model: Once satisfied with the results, it is time to utilize your model. You can do that locally on your machine or upload your model to the cloud to be accessed through an API.

Supervised learning is the same as how we learn in school: student-teacher learning. Steps: - The teacher gives us a task (input given to a model) - Based on our knowledge, we try to solve the problem and generate a result (forward pass) - The teacher compares our generated result and the correct result (loss function) - Based on the comparison, the teacher gives us feedback about how correct or wrong we were (optimizer showing grad to be corrected) - We modify our knowledge to be able to correctly do the task next time (backpropagation for model weight update)

In understanding how supervised learning operates within AI, it's vital to grasp its sequential nature, akin to teaching a new skill. Selecting the right model is the starting block, much like choosing the correct teaching method. Training, the heart of the process, is where the model learns from examples, akin to a student learning through practice. The evaluation phase is similar to an exam, testing the model's knowledge on new, unseen data. It's crucial to choose the right metrics here, as they define what 'success' looks like. Lastly, applying the model in real-life scenarios is where theory meets practice. It's important to remember that, much like in real life, adapting to new information or conditions is key to continued success.

Whenever you have a set of inputs and desired outputs, you can try supervised learning. It can be applied anywhere; just a few examples include: * Marketing: What will be the number of impressions of this social media post? * Sales: How many units will be sold of this new product? * Human Resources: What is the chance of that employee leaving the company? * Medicine: What is the diagnosis of this patient? Will this new treatment work? * Social Media: What is the overall sentiment in these 1000 comments?

The top use cases of supervised learning in business are: 1/ Customer Churn Prediction: Identifying customers at risk of leaving a service or product based on their behavior and characteristics, allowing businesses to take proactive measures. 2/ Sales Forecasting: Predicting future sales based on historical data, market trends, and other relevant factors, aiding in inventory management and resource allocation. 3/ Fraud Detection in financial transactions by learning patterns from past instances and flagging suspicious behavior 4/Customer Segmentation based on similar characteristics to services to specific segments. 5/ Credit Scoring by analyzing financial and personal information, streamlining the loan approval process.

Supervised learning, particularly in the medical device sector, plays a crucial role in advancing autosegmentation of medical images, especially concerning anatomy. In our regulated industry, the integrity and thorough vetting of training data by domain experts is not just a best practice but a necessity. This rigorous process ensures the safety and ethical deployment of AI models. It aligns with, and arguably should be a cornerstone of, FDA requirements. Leveraging supervised learning, we can enhance precision in surgeries and patient-specific treatments, revolutionizing preoperative planning, robotic surgery, and postoperative care.

Supervised learning finds application in various fields. In healthcare, it aids in disease diagnosis. In finance, it predicts market trends. Image and speech recognition, language translation, and autonomous vehicles leverage supervised learning for accurate predictions.

Supervised learning transforms complex data into actionable insights. My research demonstrates its versatility: - Online Behavior: Detecting Malay-language hate speech using a tailored dataset and the XLCaps deep learning model. - Industrial Safety: Improving fault detection in machinery with a hybrid machine and deep learning ensemble model, enhancing accuracy and efficiency. - Financial Security: Predicting payment card fraud by applying machine learning models to transaction data, elevating detection capabilities. These examples highlight supervised learning's capacity to solve real-world challenges across various sectors.

I believe there's a need to address the challenge of model interpretability. This involves making the model's decisions understandable to humans, which is crucial in sensitive applications like healthcare or finance.

For managers, training AI using supervised learning is a vital new skill. It's about shaping AI to be a valuable and effective business team member. AI in the Team: AI is more than just tech—it's like a new, smart team member. Training AI: Just as managers train staff, they must learn how to train AI. Why It's Important: - Aligning AI with Goals: Ensuring AI actions support business objectives. - Enhancing Business Operations: From automating tasks to providing insights. Keys to Train AI: 1. Select Suitable Data: Choose the right examples for AI to learn from. 2. Define Clear Objectives: Set specific goals for what the AI needs to achieve. 3. Monitor and Adjust: Continuously fine-tune AI's learning process.

Supervised learning in the context of vision problems, such as image classification or object detection, has several challenges. One is data quality and quantity. Obtaining a large and diverse dataset with accurate annotations is crucial for training supervised models. Collecting, cleaning, and labelling large volumes of data can be expensive and time-consuming, especially for complex vision tasks. Another challenging factor is overfitting. Models can easily overfit due to the high dimensionality of image data and the flexibility of NN architectures. Addressing these challenges often involves a combination of innovative algorithms, data augmentation techniques, robust architectures, regularization methods, and fine-tuning of models.

In the medical software domain, especially for preoperative planning, supervised AI learning faces specific challenges. Data quality and availability are critical; acquiring diverse, high-quality medical datasets is tough, compounded by privacy concerns. Regulatory compliance demands not just adherence to FDA standards but also a strong emphasis on model interpretability and transparency, ensuring safe, ethical AI use in healthcare. Another key challenge is integrating these AI models into clinical workflows. They must be not only accurate and reliable but also seamlessly fit into existing medical systems, enhancing healthcare delivery without disrupting routine practices.

Another aspect worth highlighting is the ethical considerations and potential biases that can arise from poorly curated datasets. When training data reflects existing biases or inequalities, the resulting models can perpetuate or even amplify these issues. For example, in facial recognition technology, a lack of diverse representation in training data can lead to models that perform poorly on certain demographic groups. This has profound implications for fairness and equity in AI applications. Moreover, the reliance on large labeled datasets can sometimes lead to privacy concerns, particularly in sensitive areas like healthcare or finance.

I would add that ethical considerations, such as ensuring fairness and avoiding bias in model predictions, are crucial in supervised learning. These aspects, alongside data quality and labeling challenges, are essential for developing effective and responsible AI systems.

Supervised learning in AI involves training a model with labeled data, where input-output pairs guide the algorithm to generalize patterns. Using optimization techniques, like gradient descent, the model adjusts parameters to minimize prediction errors. Applications include image classification (e.g., facial recognition), NLP tasks (sentiment analysis, translation), speech recognition, and predictive analytics. Challenges encompass data quality, overfitting, bias (leading to fairness concerns), and scalability issues. Building robust and ethical models requires addressing these challenges for effective deployment in various domains.

Some supervised learning models are currently being replaced by generative AI ones on specific tasks. Take for example sentiment analysis, which most state of the art Large Language Models can perform out of the box. Does that mean that supervised learning models should be discontinued? Not necessarily. Bigger more complex models are not always better for solving a problem, especially if you need to fine tune them with domain knowledge anyway. And when it comes to the cost to performance ratio, using an LLM for sentiment analysis could be like trying to use a race car in a scenario when a bicycle would do. Ultimately, it is up to each individual use case.

Supervised learning in AI is a process where models are trained using labelled data, which means the input data is paired with the correct output. This method is particularly effective for classification and regression tasks. One thing I've found helpful is ensuring the training data is as diverse and comprehensive as possible, which aids the model in learning to generalize well when faced with new, unseen data. However, the challenge lies in acquiring a sufficiently large and well-labeled dataset. Careful preparation and quality control of this data are essential for the success of a supervised learning model. #SupervisedLearning #MachineLearning #AIDevelopment

In conclusion, supervised learning is powerful for problems where the relationship between input data and the desired output can be learned from examples. Its effectiveness depends heavily on the quality and quantity of the labeled data available for training. And what about applications of supervised learning? 🔸Image Recognition: Classifying images into categories (like identifying animals in photographs). 🔸Speech Recognition: Translating spoken words into text. 🔸Medical Diagnosis: Predicting diseases based on patient data and tests. 🔸Financial Forecasting: Predicting stock prices or market trends.