Types of Machine Learning Systems

There are so many different types of Machine Learning systems that it is useful to classify them in broad categories, based on the following criteria:

    • Whether or not they are trained with human supervision (supervised, unsupervised,                               semisupervised, and Reinforcement Learning)

    • Whether or not they can learn incrementally on the fly (online versus batch learning)

    • Whether they work by simply comparing new data points to known data points, or instead by                  detecting patterns in the training data and building a predictive model, much like scientists do                  (instance-based versus model-based learning)

Machine Learning systems can be classified according to the amount and type of supervision they get during training. There are four major categories: supervised learning, unsupervised learning, semisupervised learning, and Reinforcement Learning.

Supervised Learning

In supervised learning, the training set you feed to the algorithm includes the desired solutions, called labels (Figure 1-5).

A typical supervised learning task is classification. The spam filter is a good example of this: it is trained with many example emails along with their class (spam or ham), and it must learn how to classify new emails.

Another typical task is to predict a target numeric value, such as the price of a car, given a set of features (mileage, age, brand, etc.) called predictors. This sort of task is called regression (Figure 1-6). To train the system, you need to give it many examples of cars, including both their predictors and their labels (i.e., their prices).

Here are some of the most important supervised learning algorithms :

• k-Nearest Neighbors

• Linear Regression

• Logistic Regression

• Support Vector Machines (SVMs)

• Decision Trees and Random Forests

• Neural networks

Unsupervised learning

In unsupervised learning, as you might guess, the training data is unlabeled (Figure 1-7). The system tries to learn without a teacher.

For example, say you have a lot of data about your blog’s visitors. You may want to run a clustering algorithm to try to detect groups of similar visitors (Figure 1-8). At no point do you tell the algorithm which group a visitor belongs to: it finds those connections without your help. For example, it might notice that 40% of your visitors are males who love comic books and generally read your blog in the evening, while 20% are young sci-fi lovers who visit during the weekends. If you use a hierarchical clustering algorithm, it may also subdivide each group into smaller groups. This mayhelp you target your posts for each group.

Visualization algorithms are also good examples of unsupervised learning algorithms: you feed them a lot of complex and unlabeled data, and they output a 2D or 3D representation of your data that can easily be plotted (Figure 1-9). These algorithms try to preserve as much structure as they can (e.g., trying to keep separate clusters in the input space from overlapping in the visualization) so that you can understand how the data is organized and perhaps identify unsuspected patterns.

A related task is dimensionality reduction, in which the goal is to simplify the data without losing too much information. One way to do this is to merge several correlated features into one. For example, a car’s mileage may be strongly correlated with its age, so the dimensionality reduction algorithm will merge them into one feature thatrepresents the car’s wear and tear. This is called feature extraction.

It is often a good idea to try to reduce the dimension of your training data using a dimensionality reduction algorithm before you feed it to another Machine Learning algorithm (such as a supervised learning algorithm). It will run much faster, the data will take up less disk and memory space, and in some cases it may also perform better.

Yet another important unsupervised task is anomaly detection—for example, detecting unusual credit card transactions to prevent fraud, catching manufacturing defects, or automatically removing outliers from a dataset before feeding it to another learning algorithm. The system is shown mostly normal instances during training, so it learns to recognize them; then, when it sees a new instance, it can tell whether it looks like a normal one or whether it is likely an anomaly (see Figure 1-10). A very similar task is novelty detection: it aims to detect new instances that look different from all instances in the training set.

Finally, another common unsupervised task is association rule learning, in which the goal is to dig into large amounts of data and discover interesting relations between.attributes. For example, suppose you own a supermarket. Running an association rule on your sales logs may reveal that people who purchase barbecue sauce and potato chips also tend to buy steak. Thus, you may want to place these items close to one another.

Here are some of the most important unsupervised learning algorithms:

• Clustering

    —K-Means

    —DBSCAN

    —Hierarchical Cluster Analysis (HCA)

• Anomaly detection and novelty detection

    —One-class SVM

    —Isolation Forest

• Visualization and dimensionality reduction

    —Principal Component Analysis (PCA)

    —Kernel PCA

    —Locally Linear Embedding (LLE)

    —t-Distributed Stochastic Neighbor Embedding (t-SNE)

• Association rule learning

    —Apriori

    —Eclat

Semisupervised learning

Since labeling data is usually time-consuming and costly, you will often have plenty of unlabeled instances, and few labeled instances. Some algorithms can deal with data that’s partially labeled. This is called semisupervised learning (Figure 1-11).

Some photo-hosting services, such as Google Photos, are good examples of this. Once you upload all your family photos to the service, it automatically recognizes that the same person A shows up in photos 1, 5, and 11, while another person B shows up in photos 2, 5, and 7. This is the unsupervised part of the algorithm (clustering). Now all the system needs is for you to tell it who these people are. Just add one label per person and it is able to name everyone in every photo, which is useful for searching photos.

Most semisupervised learning algorithms are combinations of unsupervised and supervised algorithms. For example, deep belief networks (DBNs) are based on unsupervised components called restricted Boltzmann machines (RBMs) stacked on top of one another. RBMs are trained sequentially in an unsupervised manner, and then the whole system is fine-tuned using supervised learning techniques.

Reinforcement Learning

Reinforcement Learning is a very different beast. The learning system, called an agent in this context, can observe the environment, select and perform actions, and get rewards in return (or penalties in the form of negative rewards, as shown in Figure 1-12). It must then learn by itself what is the best strategy, called a policy, to get the most reward over time. A policy defines what action the agent should choose when it is in a given situation.

For example, many robots implement Reinforcement Learning algorithms to learn how to walk. DeepMind’s AlphaGo program is also a good example of Reinforcement Learning: it made the headlines in May 2017 when it beat the world champion Ke Jie at the game of Go. It learned its winning policy by analyzing millions of games, and then playing many games against itself.

🔹 Summary of Machine learning systems  Based on Learning Style

a. Supervised Learning

• Definition: The model is trained on labeled data (i.e., input-output pairs).

• Goal: Learn a mapping from inputs to outputs.

• Examples:

  • Spam detection (email → spam/not spam)

  • House price prediction (features → price)

• Algorithms: Linear Regression, Decision Trees, Support Vector Machines, Neural Networks

b. Unsupervised Learning

• Definition: The model learns patterns from unlabeled data.

• Goal: Discover hidden structures or groupings in data.

• Examples:

  • Customer segmentation

  • Anomaly detection

• Algorithms: K-Means Clustering, Principal Component Analysis (PCA), DBSCAN

c. Semi-Supervised Learning

• Definition: Uses a small amount of labeled data with a large amount of unlabeled data.

• Goal: Improve learning accuracy when labeling data is expensive or time-consuming.

• Example: Text classification with a few labeled examples

d. Reinforcement Learning

• Definition: The model learns by interacting with an environment and receiving feedback as rewards or penalties.

• Goal: Learn a policy to maximize cumulative reward over time.

• Examples:

  • Game playing (e.g., Chess, Go)

  • Autonomous driving

• Algorithms: Q-Learning, Deep Q Networks (DQN), Policy Gradient Methods

Batch and Online Learning

Another criterion used to classify Machine Learning systems is whether or not the system can learn incrementally from a stream of incoming data.

Batch Learning

Batch learning is a machine learning approach where the system is trained using the entire dataset at once. This process is usually performed offline, as it requires significant time and computational resources. Once the model is trained, it is deployed into production and used for predictions without learning from new data in real-time. To incorporate new information (like newly observed spam), the entire system must be retrained from scratch using both the old and new data, and then the updated model replaces the previous one.

Although this process can be automated, retraining on the full dataset frequently can be costly and time-consuming, especially as the size of the data grows. This makes batch learning unsuitable for systems that need to respond to rapidly changing data or operate with limited computational resources, such as mobile apps or autonomous devices. In such scenarios, incremental or online learning methods, which update the model continuously or in small steps, are more practical and efficient.

Online learning

In online learning, you train the system incrementally by feeding it data instances sequentially, either individually or in small groups called mini-batches. Each learning step is fast and cheap, so the system can learn about new data on the fly, as it arrives (see Figure 1-13).

Online learning is great for systems that receive data as a continuous flow (e.g., stock prices) and need to adapt to change rapidly or autonomously. It is also a good option if you have limited computing resources: once an online learning system has learned about new data instances, it does not need them anymore, so you can discard them(unless you want to be able to roll back to a previous state and “replay” the data). This can save a huge amount of space.

Online learning algorithms can also be used to train systems on huge datasets that cannot fit in one machine’s main memory (this is called out-of-core learning). The algorithm loads part of the data, runs a training step on that data, and repeats the process until it has run on all of the data (see Figure 1-14).

Out-of-core learning is usually done offline (i.e., not on the live system), so online learning can be a confusing name. Think of it as incremental learning.

One important parameter of online learning systems is how fast they should adapt to changing data: this is called the learning rate. If you set a high learning rate, then your system will rapidly adapt to new data, but it will also tend to quickly forget the old data (you don’t want a spam filter to flag only the latest kinds of spam it was shown). Conversely, if you set a low learning rate, the system will have more inertia; that is, it will learn more slowly, but it will also be less sensitive to noise in the new data or to sequences of non representative data points (outliers).

A big challenge with online learning is that if bad data is fed to the system, the system’s performance will gradually decline. If it’s a live system, your clients will notice. For example, bad data could come from a malfunctioning sensor on a robot, or from someone spamming a search engine to try to rank high in search results. To reduce this risk, you need to monitor your system closely and promptly switch learning off (and possibly revert to a previously working state) if you detect a drop in performance. You may also want to monitor the input data and react to abnormal data (e.g.,using an anomaly detection algorithm).

Instance-Based Versus Model-Based Learning

One more way to categorize Machine Learning systems is by how they generalize.Most Machine Learning tasks are about making predictions. This means that given a number of training examples, the system needs to be able to make good predictions for (generalize to) examples it has never seen before. Having a good performance measure on the training data is good, but insufficient; the true goal is to perform well on new instances.

There are two main approaches to generalization: instance-based learning and model-based learning.

Instance-based learning

Possibly the most trivial form of learning is simply to learn by heart. If you were to create a spam filter this way, it would just flag all emails that are identical to emails that have already been flagged by users—not the worst solution, but certainly not the best.

Instead of just flagging emails that are identical to known spam emails, your spam filter could be programmed to also flag emails that are very similar to known spam emails. This requires a measure of similarity between two emails. A (very basic) similarity measure between two emails could be to count the number of words they have in common. The system would flag an email as spam if it has many words in common with a known spam email.

This is called instance-based learning: the system learns the examples by heart, then generalizes to new cases by using a similarity measure to compare them to the learned examples (or a subset of them). For example, in Figure 1-15 the new instance would be classified as a triangle because the majority of the most similar instances belong to that class.

Model-based learning

Another way to generalize from a set of examples is to build a model of these examples and then use that model to make predictions. This is called model-based learning (Figure 1-16).