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Supervised learning and Unsupervised learning are two popular approaches in Machine Learning. The simplest way to distinguish between supervised and unsupervised learning is the type of training dataset and the way the models are trained. However, there are other differences, which are further discussed in the chapter.

What is Supervised Learning?

Supervised Learning is a machine learning approach that uses labeled datasets to train the model, making it ideal for tasks like classifying data or predicting output. Supervised learning is categorized into two types −

1. Classification

Classification uses algorithms to predict categorical values, such as determining whether an email is spam or not or whether it is true or false. The algorithm learns to map each input to its corresponding output label. Some common algorithms include K-Nearest Neighbors, Random forests and Decision trees.

2. Regression

Regression is a statistical approach to analyze the relationship between data points. It can be used to forecast house prices based on features like location and size or estimate future sales. Some common algorithms include linear regression, polynomial regression, and logistic regression.

What is Unsupervised Learning?

Unsupervised Learning is a machine learning approach used to train models on raw and unlabeled data. This approach is often used to identify patterns in the data without human supervision. Unsupervised learning models are used to for the below tasks −

1. Clustering

This task uses unsupervised learning models to group data points into clusters based on their similarities. Popular algorithm used is the K-means clustering.

2. Association

This is another type of unsupervised learning that uses pre-defined rules to group data points into a cluster. It is commonly used in Market Basket Analysis, and the main algorithm behind this task is Apriori Algorithm.

3. Dimensionality Reduction

This method of unsupervised learning is used to reduce the size of a dataset by removing features that are not necessary without compromising the originality of the data.

Differences between Supervised and Unsupervised Learning

The table below shows some key differences between supervised and unsupervised machine learning −

Supervised or Unsupervised Learning – Which to Choose?

Choosing the right approach is crucial and will also determine the efficiency of the outcome. To decide on which learning approach is best, the following things should be considered −

• Dataset − Evaluate the data, whether it is labeled or unlabeled. You will also need to assess whether you have the time, resources and expertise to support labeling.
• Goals − It is also important to define the problem you are trying to solve and the solution you are trying to opt for. It might be classification, discovering new patterns or insights in the data or creating a predictive model.
• Algorithm − Review the algorithm by making sure that it matches required dimensions, such as attributes and number of features. Also, evaluate if the algorithm can support the volume of the data.

Semi-supervised Learning

Semi-supervised learning is the safest medium if you are in a dilemma about choosing between supervised and unsupervised learning. This learning approach is a combination of both supervised and unsupervised learning, where a minor part of the dataset used is labeled and the major part is unlabeled. This is ideal when you have a high volume of data that makes it difficult to identify relevant features.

What is Reinforcement Learning?

Reinforcement learning is a machine learning approach where an agent (software entity) is trained to interpret the environment by performing actions and monitoring the results. For every good action, the agent gets positive feedback and for every bad action the agent gets negative feedback. It’s inspired by how animals learn from their experiences, making decisions based on the consequences of their actions.

The following diagram shows a typical reinforcement learning model −

In the above diagram, the agent is represented in a particular state. The agent takes action in an environment to achieve a particular task. As a result of the performed task, the agent receives feedback as a reward or punishment.

How Does Reinforcement Learning Work?

In reinforcement learning, there would be an agent that we want to train over a period of time so that it can interact with a specific environment. The agent will follow a set of strategies for interacting with the environment and then after observing the environment it will take actions regarding the current state of the environment. The agent learns how to make decisions by receiving rewards or penalties based on its actions.

The working of reinforcement learning can be understood by the approach of a master chess player.

• Exploration − Just like how a chess play considers various possible move and their outcome, the agent also explores different actions to understand their effects and learns which action would lead to better result.
• Exploitation − The chess player also uses intuition, based on past experiences to make decisions that seem right. Similarly, the agent uses knowledge gained from previous experiences to make best choices.

Key Elements Reinforcement Learning

Beyond the agent and the environment, one can identify four main sub elements of reinforcement learning system −

• Policy − It defines the learning agent’s way of behaving at a given time. A policy is a mapping from perceived states of the environment to actions to be taken when in those states.
• Reward Signal − It defines the goal of a reinforcement learning problem. It is a numerical score received to the agent by the environment. This reward signal defines what are the good and bad events for the agent.
• Value function − It specifies what is good in the long run. The value is the total amount of reward an agent can expect to accumulate over the future, starting from that state.
• Model − Models are used for planning, which means deciding on a course of action by considering possible future situations before they are actually experienced.

Markov Decision Processes(MDP) provide a mathematical framework for modeling decision-making in an environment with states, actions, rewards, probability. Reinforcement learning uses MDP to understand how an agent should act to maximize rewards and to find the best strategies for decision making.

Markov Decision Processes (MDP)

Reinforcement learning uses the mathematical framework of Markov decision processes(MDP) to define the interaction between learning agent and environment. Some important concepts and components of MDP are −

• States(S) − Represents all the situations in which an agent can find itself.
• Action(A) − The choices available for the agent from the gives states.
• Transition Probabilities(P) − The likelihood of moving from one state to another as a result of a specific action.
• Rewards(R) − Feedback received after transitioning to a new state due to an action, indication the outcome’s desirability.
• Policy( ) − A strategy that defines the action to take in each state for achieving a reward.

Steps in Reinforcement Learning Process

Here are the major steps involved in reinforcement learning methods −

• Step 1 − First, we need to prepare an agent with some initial set of strategies.
• Step 2 − Then observe the environment and its current state.
• Step 3 − Next, select the optimal policy regards the current state of the environment and perform important action.
• Step 4 − Now, the agent can get corresponding reward or penalty as per accordance with the action taken by it in previous step.
• Step 5 − Now, we can update the strategies if it is required so.
• Step 6 − At last, repeat steps 2-5 until the agent got to learn & adopt the optimal policies.

Types of Reinforcement Learning

There are two types of Reinforcement learning:

• Positive Reinforcement − When an agent performs an action that is desirable or leads to a good out, it receives a rewards which increase the livelihood of that action being repeated.
• Negative Reinforcement − When an agent performs an action to avoid a negative outcome, the negative stimulus is removed. For example, if a robot is programmed to avoid an obstacle and successfully navigates away from it, the threat associated with action is removed. And the robot more likely avoids that action in the future.

Types of Reinforcement Learning Algorithms

There are various algorithms used in reinforcement learning such as Q-learning, policy gradient methods, Monte Carlo method and many more. All these algorithms can be classified into two broad categories −

• Model-free Reinforcement Learning − It is a category of reinforcement learning algorithms that learns to make decisions by interacting with the environment directly, without creating a model of the environment’s dynamics. The agent performs different actions multiple times to learn the outcomes and creates a strategy (policy) that optimizes its reward points. This is ideal for changing, large or complex environments.
• Model-based Reinforcement Learning − This category of reinforcement learning algorithms involves creating a model of the environment’s dynamics to make decisions and improve performance. This model is ideal when the environment is static, and well-defined, where real-world environment testing is difficult.

Advantages of Reinforcement Learning

Some of the advantages of reinforcement learning are −

• Reinforcement learning doesn’t require pre-defined instructions and human intervention.
• Reinforcement learning model can adapt to wide range of environments including static and dynamic.
• Reinforcement learning can be used to solve wide range of problems, including decision making, prediction and optimization.
• Reinforcement learning model gets better as it gains experience and fine-tunes.

Disadvantages of Reinforcement Learning

Some of the disadvantages of reinforcement learning are −

• Reinforcement learning depends on the quality of the reward function, if it is poorly designed, the model can never get better with its performance.
• The designing and tuning of reinforcement learning can be complex and requires expertise.

Applications of Reinforcement Learning

Reinforcement learning has a wide range of applications across various fields. Some major applications are −

1. Robotics

Reinforcement learning is generally concerned with decision-making in unpredictable environments. This is the most used approach especially for complicated tasks, such as replicating human behavior, manipulation, navigation and locomotion. This approach also allows robots to adapt to new environments through trial and error.

2. Natural Language Processing (NLP)

In Natural Language Processing (NLP), Reinforcement learning is used to enhance the performance of chatbots by managing complex dialogues and improving user interactions. Additionally, this learning approach is also used to train models for tasks like summarizations.

Reinforcement Learning Vs. Supervised learning

Supervised learning and Reinforcement learning are two distinct approaches in machine learning. In supervised learning, a model is trained on a dataset that consists of both input and its corresponding outputs for predictive analysis. Whereas, in reinforcement learning an agent interacts with an environment, learning to make decisions by receiving feedback in the form of rewards or penalties, aiming to maximize cumulative rewards. Another difference between these two approaches is the tasks that they are ideal for. While supervised learning is used for tasks that are often with clear, structured output, reinforcement learning is used for complex decision making tasks with optimal strategies.

Semi-supervised learning is a type of machine learning that is neither fully supervised nor fully unsupervised. The semi-supervised learning algorithms basically fall between supervised and unsupervised learning methods.

In semi-supervise learning, mahcine learning algorithms are trained on datasets that contains both labeled and unlabeled data. Semi-supervised learning is generally used when we have a huge set of unlabeled data available. In any supervised learning algorithm, the available data has to be manually labelled which can be quite an expensive process. In contrast, the unlabeled data used in unsupervised learning has limited applications. Hence, semi-supervised learning algorithms were developed which can provide a perfect balance between the two.

What is Semi-Supervised Learning?

Semi-supervised learning is a machine learning approch or technique that works in combination of supervised and unsupervised learning. In semi-supervised learning, the machine learning alogrithms are trained on a small amount of labeled data and a large amount of unlabeled data.

The goal of semi-supervised learning is to develop an algorithm to divide the entire data into different clusters and the data points closer to each other most likely share the same output label, and then to classify the cluster into a predefined category.

We can summarize semi-supervised learning as

• a machine learning approach or technique that
• combines supervised learning and unsuprvised learning
• to train ML models by using labeled and unlabled data
• to perform classification and regreesion related tasks.

Semi-supervised Learning Vs. Supervised Learning

The primary difference between supervised learning and semi-supervised is the dataset that is used to train the model. In supervised learning, the model is trained on a dataset that consists of input and each of it is paired with a predefined label i.e, the features and their corresponding target label is provided. This allows for more accurate prediction or classification. Whereas, in semi-supervised learning the dataset consists of a minor amount of labeled data and a major amount of unlabeled data. The model is initially trained on labeled data, then uses these insights to train unlabeled data to discover additional patterns.

Semi-supervised Learning Vs. Unsupervised Learning

Unsupervised learning trains a model only on unlabeled dataset, aiming to identify groups with common features within the dataset. In contrast, semi-supervised learning uses a mix of labeled data(small amount) and unlabeled data(large amount). In unsupervised learning, the data points in the dataset are grouped into clusters based on common features, where as semi-supervised learning is much efficient since each cluster is allotted a pre-defined label since it train on labeled data along with unlabeled data.

When to Choose Semi-Supervised Learning?

Situations where obtaining a sufficient amount of labeled data is difficult and expensive, but gathering unlabeled data is much easier. In such scenarios, neither fully supervised nor unsupervised learning methods will provide accurate outcomes. This is where semi-supervised learning methods can be implemented.

How Does Semi-Supervised Learning Work?

Semi-supervised learning generally uses small supervised learning component, i.e., small amount of pre-labeled annotated data and large unsupervised learning component, i.e., lots of unlabeled data for training.

In machine learning, we can follow any of the following approaches for implementing semi-supervised learning methods −

• The first and simple approach is to build the supervised model based on a small labeled and annotated data and then build the unsupervised model by applying the same to the large amounts of unlabeled data to get more labeled samples. Now, train the model on them and repeat the process.
• The second approach needs some extra efforts. In this approach, we can first use the unsupervised methods to cluster similar data samples, annotate these groups and then use a combination of this information to train the model.

In Semi-supervised learning, the unlabeled data used should be relevant to the task the model is trained to perform. In mathematical terms, the input data’s distribution p(x) must contain information about the posterior distribution p(y|x), which represents the probability of a given data point (x) belonging to a certain class (y).

There are certain assumptions held for the working of semi-supervised learning like −

• Smoothness Assumption
• Cluster Assumption
• Low Density Separation
• Manifold Assumptions

Let us have a brief understanding about the above listed assumptions.

Smoothness Assumption

This assumption states that two data points x1 and x2 in a high-density region (belong to same cluster) are close, so should be the corresponding output labels y1 and y2. On the other hand, if the data points are in low density region, their outputs need not be close

Cluster Assumption

Cluster assumption states that when data points are in the same cluster, they are likely to be of the same class. Unlabeled data should aid in finding the boundary of each cluster more accurately using clustering algorithms. Additionally, the labeled data points should be used to assign a class for each cluster.

Low Density Separation

Low Density Separation assumption states that the decision boundary should lie in the low density region. Consider digit recognition, for instance, one wants to distinguish a handwritten digit 0 against digit 1. A sample point taken exactly from the decision boundary will be between a 0 and a 1, most likely a digit looking like a very elongated zero. But the probability that someone wrote this weird digit is very small.

Manifold Assumptions

This assumption forms the basis of several semi-supervised learning methods, it states that in a higher-dimensional input space, there are several lower dimensional manifolds where all data points exist, and data points with the same label are located on the same manifold.

Semi-supervised Learning Techniques

Semi-supervised learning uses several techniques to bring out the best from both labeled and unlabeled data for accurate outcomes. Some popular techniques include −

Self-training

Self-training is a process in which any supervised method like classification and regression, can be modified to work in a semi-supervised manner, taking insights from both labeled and unlabeled data.

Co-training

This approach is an improved version of Self-training approach, where the idea wa to make use of different “views” on the data that is to be classified. This is ideally used for web content classification where, a web page can be represented by the text on the page, and can also be represented by the hyperlinks referring to the pages. Unlike the typical process, the co-training approach trains two individual classifiers based on two views of data to improve learning performance.

Graph based label propagation

The most efficient way to run semi-supervised learning, it models data as graphs where nodes represent data points and edges represent similarities between them, and then the label propagation algorithm is applied. In this approach, labeled data points propagate their labels through the graph, influencing the neighboring nodes. The labels are iteratively updated, allowing the model to assign labels to unlabeled nodes.

Challenges of Semi-supervised Learning

Semi-supervised learning requires only a small amount of labeled data along side large set of unlabeled data, reducing the cost and need of manual labeling. In contrast, there are a few challenges that has be addressed like −

• Quality of data − The efficiency of semi-supervised learning depends on the quality of unlabeled data. If the unlabeled data is noisy or irrelevant, there are chances that it might to lead to incorrect predictions and poor performance.
• Variation in the data − Semi-supervised learning models are more prone to distribution shifts between the labeled and unlabeled data. For examples, a model is trained on labeled dataset that consists clear high quality images where as the if the unlabeled data contains images from captured from surveillance cameras, it would be difficult to generalize from the labeled to the unlabeled images, impacting the outcomes.

Applications of Semi-supervised Learning

Semi-supervised machine learning finds its application in text classification, image classification, speech analysis, anomaly detection, etc. where the general goal is to classify an entity into a predefined category. Semi-supervised algorithm assumes that the data can be divided into discrete clusters and the data points closer to each other are more likely to share the same output label.

Some popular applications of semi-supervised learning are −

• Speech Recognition − Labeling audio data is a time consuming task, semi-supervised techniques improve speech models combining unlabeled audio data alongside limited transcribed speech. This enhances the accuracy in recognizing spoken language.
• Web Content Classification − With billions of websites, manually labeling content is impractical. Semi-supervised Learning helps classify web content efficiently, improving search engines like Google in ranking and produces relevant content to user queries.
• Text Document Classification − Semi-supervised Learning is used to classify text by training on small set of labeled documents and large corpus of unlabeled text. The model first learns from labeled data to gain insights and then use it to classify text. This learning methods helps improve the accuracy of classification without the need for extensive labeled datasets.

What is Unsupervised Machine Learning?

Unsupervised learning, also known as unsupervised machine learning, is a type of machine learning that learns patterns and structures within the data without human supervision. Unsupervised learning uses machine learning algorithms to analyze the data and discover underlying patterns within unlabeled data sets.

Unlike supervised machine learning, unsupervised machine learning models are trained on unlabeled dataset. Unsupervised learning algorithms are handy in scenarios in which we do not have the liberty, like in supervised learning algorithms, of having pre-labeled training data and we want to extract useful patterns from input data.

We can summarize unsupervised learning as −

• a machine learning approach or type that
• uses machine learning algorithms
• to find hidden patterns or structures
• within the data without human supervision.

There are many approaches that are used in unsupervised machine learning. Some of the approaches are association, clustering, and dimensionality reduction. Some examples of unsupervised machine learning algorithms include K-means clustering, K-nearest neighbors, etc.

In regression, we train the machine to predict a future value. In classification, we train the machine to classify an unknown object in one of the categories we define. In short, we have been training machines so that it can predict Y for our data X. Given a huge data set and not estimating the categories, it would be difficult for us to train the machine using supervised learning. What if the machine can look up and analyze the big data running into several Gigabytes and Terabytes and tell us that this data contains so many distinct categories?

As an example, consider the voters data. By considering some inputs from each voter (these are called features in AI terminology), let the machine predict that there are so many voters who would vote for X political party and so many would vote for Y, and so on. Thus, in general, we are asking the machine given a huge set of data points X, What can you tell me about X?. Or it may be a question like What are the five best groups we can make out of X?. Or it could be even like What three features occur together most frequently in X?.

This is exactly what Unsupervised Learning is all about.

How does Unsupervised Learning Work?

In unsupervised learning, machine learning algorithms (called self-learning algorithms) are trained on unlabeled data sets i.e, the input data is not categorized. Based on the tasks, or machine learning problems such as clustering, associations, etc. and the data sets, the suitable algorithms are chosen for the training.

In the training process, the algorthims learn and infer their own rules on the basis of the similarities, patterns and differences of data points. The algorithms learn without any labels (target values) or pre-training.

The outcome of this training process of algorithm with data sets is a machine learning model. As the data sets are unlabeled (no target values, no human supervision), the model is unsupervised machine learning model.

Now the model is ready to perform the unsupervised learning tasks such as clustering, association, or dimensionality reduction.

Unsupervised learning models is suitable complex tasks, like organizing large datasets into clusters.

Unsupervised Machine Learning Methods

Unsupervised learning methods or approaches are broadly categorized into three categories − clustering, association, and dimensionality reduction. Let us discuss these methods briefly and list some related algorithms −

1. Clustering

Clustering is a technique used to group a set of objects or data points into clusters based on their similarities. The goal of this technique is to make sure that the data points within the same cluster should have more similarities than those in other clusters.

Clustering is sometimes called unsupervised classification because it produces the same result as classification does but without having predefined classes.

Clustering is one of the popular unsupervised learning approaches. There are several unsupervised learning algorithms used for clustering like −

• K-Means Clustering − This algorithm is used to assign data points to one among the K clusters based on its distance from the center of the cluster. After assigning each data point to a cluster, new centroids are recalculated. This is an iterative process until the centroids no longer change. This shows that the algorithm is efficient and the clusters are stable.
• Mean Shift Algorithm − It is a clustering technique that identifies clusters by finding high data density areas. It is an iterative process, where mean of each data point is shifted towards the densest area of the data.
• Gaussian Mixture Models − It is a probabilistic model that is a combination of multiple Gaussian distributions. These models are used to determine which determination a given data belongs to.

2. Association Rule Mining

This is rule based technique that is used to discover associations between parameters in large dataset. It is popularly used for Market Basket Analysis, allows companies to make decisions and recommendation engines. One of the main algorithms that is used for Association Rule Mining is the Apriori algorithm.

Apriori Algorithm

Apriori algorithm is a technique used in unsupervised learning to identify data points that are frequently repeated and discover association rules within transactional data.

3. Dimensionality Reduction

As the name suggests, dimensionality reduction is used to reduce the number of feature variables for each data sample by selecting set of principal or representative features.

A question arises here is that, why we need to reduce the dimensionality? The reason behind this is the problem of feature space complexity which arises when we start analyzing and extracting millions of features from data samples. This problem generally refers to “curse of dimensionality”. Some popular algorithms in unsupervised learning that are used for dimensionality reduction are −

• Principle Component Analysis
• Missing Value Ratio
• Singular Value Decomposition
• Autoencoders

Algorithms for Unsupervised Learning

Algorithms are very important part in machine learning model training. A machine learning algorithm is a set of instructions that a program follows to analyze the data and produce the outcomes. For specific tasks, suitable machine learning algorithms are selected and trained on the data.

Algorithms used in unsupervised learning generally fall under one of the three categories − clustering, association, or dimensionality reduction. The following are the most used unsupervised learning algorithms −

• K-Means Clustering
• Hierarchical Clustering
• Mean-shift Clustering
• DBSCAN Clustering
• HDBSCAN Clustering
• BIRCH Clustering
• Affinity Propagation
• Agglomerative Clustering
• Apriori Algorithm
• Eclat algorithm
• FP-growth algorithm
• Principal Component Analysis(PCA)
• Autoencoders
• Singular value decomposition (SVD)

Advantages of Unsupervised Learning

Unsupervised learning has many advantages that make it particularly purposeful in various tasks −

• No labeled data required − Unsupervised learning doesn’t require a labeled dataset for training, which makes it easier and cheaper to use.
• Discovers hidden patterns − It helps in recognizing patterns and relationships in large data, which can lead to gaining insights and efficient decision-making.
• Suitable for complex tasks − It is efficiently used for various complex tasks like clustering, anomaly detection, and dimensionality reduction.

Disadvantages of Unsupervised Learning

While unsupervised learning has many advantages, some challenges can occur too while training the model without human intervention. Some of the disadvantages of unsupervised learning are:

• Difficult to evaluate − Without labeled data and predefined targets, it would be difficult to evaluate the performance of unsupervised learning algorithms.
• Inaccurate outcomes − The outcome of an unsupervised learning algorithm might be less accurate, especially if the input data has noise and also since the data is not labeled, the algorithms do not know the exact output.

Applications of Unsupervised Learning

Unsupervised learning provides a path for businesses to identify patterns in large volumes of data. Some real-world applications of unsupervised learning are:

• Customer Segmentation − In business and retail analysis, unsupervised learning is used to group customers into segments based on their purchases, past activity, or preferences.
• Anomaly Detection − Unsupervised learning algorithms are used in anomaly detection to identify unusual patterns, which is crucial for fraud detection in financial transactions and network security.
• Recommendation Engines − Unsupervised learning algorithms help to analyze large customer data to gain valuable insights and understand patterns. This can help in target marketing and personalization.
• Natural Language Processing− Unsupervised learning algorithms are used for various applications. For example, google used to categorize articles in the news section.

What is Anomaly Detection?

This unsupervised ML method is used to find out occurrences of rare events or observations that generally do not occur. By using the learned knowledge, anomaly detection methods would be able to differentiate between anomalous or normal data points.

Some of the unsupervised algorithms, like clustering and KNN, can detect anomalies based on the data and its features.

Supervised Vs. Unsupervised Learning

Supervised learning algorithms are trained using labeled data. But there might be cases where data might not be labeled, so how do you gain insights from data that is unlabeled and messy? Well, to solve these types of cases, unsupervised learning is used. We have done a detailed analysis on comparison between supervised and unsupervised learning in supervised vs. unsupervised learning chapter.