Table of Contents
This diagram illustrates the hierarchy within Artificial Intelligence (AI), showcasing how Machine Learning (ML) and Deep Learning (DL) fit within AI. It highlights various deep learning models, such as Feedforward Neural Networks, Convolutional Neural Networks, and Generative Adversarial Networks, as well as learning types like Supervised, Unsupervised, and Reinforcement Learning. Each layer represents increasingly specialized areas, with AI at the top as the broadest field, narrowing down to specific algorithms and techniques within DL.
1. Machine Learning
Machine Learning (ML) is a subset of AI that focuses on developing algorithms that enable machines to learn from data and improve their performance over time without being programmed for each task. Within ML, there are different types of learning.
1.1 Supervised Learning
Supervised learning is a type of machine learning where we work with a dataset that includes the input data (features) and the correct answers (labels). The goal is to train a model to predict the labels when given new input data. Features are individual information or variables used as inputs to make predictions. Think of features as the characteristics or attributes of the data that you believe will help make a prediction.
For example, in a dataset about dogs, the features might include the animal’s size, the shape of its ears, the length of its tail and coat, and the shape of its head. The label could be the dog’s breed, the specific outcome, or the value you want the model to predict based on the features.
To make this prediction process happen, you need a large dataset containing many examples of dogs with known features and labels. This data is then fed into a model, which uses algorithms to learn from the patterns in the data. The model uses this learning to predict new data where the label is unknown, such as identifying a dog’s breed based on its physical characteristics.
Feature: Size
Feature: Ear Shape
Feature: Tail Length
Feature: Coat Length
Feature: Head Shape
Model
Predict: Australian Shepherd
Predict: Cocker Spaniel
Predict: Golden Retriever
Predict: Pug
Example Training Data
| Image | Size | Ear Shape | Tail Length | Coat Length | Head Shape | Breed (Label) |
|---|---|---|---|---|---|---|
 |
Medium | Semi-Erect | Long | Medium | Square | Australian Shepherd |
 |
Medium | Floppy | Medium | Long | Round | Cocker Spaniel |
 |
Large | Floppy | Long | Long | Broad | Golden Retriever |
 |
Small | Button | Curled | Short | Round | Pug |
1.1.1 Regression
Regression is a type of supervised learning that aims to predict a continuous numerical value based on input features. For example, let’s say we want to predict the selling price of a house. In a hypothetical scenario, the house’s price depends on location, square footage, age, condition, and the number of bathrooms and bedrooms.
Example Dataset
| Bedrooms | Square Footage | Location | Age of House (Years) | Bathrooms | Condition | Selling Price ($) |
|---|---|---|---|---|---|---|
| 3 | 1500 | 1 | 10 | 2 | 3 | 600,000 |
| 4 | 2000 | 2 | 5 | 3 | 3 | 750,000 |
| 2 | 1000 | 1 | 20 | 1 | 3 | 450,000 |
| 5 | 3000 | 3 | 2 | 4 | 4 | 1,400,000 |
In this dataset:
Location is encoded as a numerical value ranging from 1 to 3: 1=Urban, 2=Suburban, 3 = Rural
Condition is also encoded numerically, where: 1 = Poor, 2 = Fair, 3 = Good, 4 = Excellent
Each house in the dataset is represented as a vector of features. For example:
- House 1:
[3, 1500, 1, 10, 2, 3] - House 2:
[4, 2000, 2, 5, 3, 3]…
Using this dataset, you can train a regression model. The model learns to associate the house’s features (like the number of bedrooms, bathrooms, and square footage) with the target value (selling price).
The model might learn an equation like this:
\[ \text{Price} = w_1 \times \text{Bedrooms} + w_2 \times \text{Square Footage} + w_3 \times \text{Location} + w_4 \times \text{Age of House} + w_5 \times \text{Bathrooms} + w_6 \times \text{Condition} + b \]
\[ w_1, w_2, \dots, w_6 \text{ are the weights learned by the model, representing how much each feature impacts the price.} \]
\[ b \text{ is the bias term.} \]
Once the model is trained, you can use it to predict the price of a new house.
For example, consider a new house with the following features:
- Bedrooms: 3
- Square Footage: 1800
- Location: Suburban (encoded as 2)
- Age of House: 8 years
- Bathrooms: 2
- Condition: Good (encoded as 3)
These features can be represented as a vector: [3,1800,2,8,2,3][3, 1800, 2, 8, 2, 3][3,1800,2,8,2,3]
The model processes these inputs and predicts the house price, for example, $650,000.
Regression is often used to predict future stock prices, housing prices, GDP, and economic growth, forecast temperatures, and calculate insurance premiums.
1.1.2 Classification
Imagine you have a dataset about dogs, where the features include the dog’s size, the shape of its ears, the length of its tail and coat, and the shape of its head. If you want to predict the breed of the dog based on these features, this would be a classification problem. In this case, the label is the breed of the dog, which is a specific category. The model learns from the data and classifies a new dog into a breed based on physical characteristics. For example, given the features, the model might classify a dog as a Pug or a Golden Retriever. (see example above)
Classification tasks are widely used in various fields to, for example, identify diseases from medical images, recognize faces, or enable autonomous vehicles to detect and classify objects on the road.
If you want to try image labeling, use CVAT, Labelme, or LabelBox tools.
1.2 Unsupervised Learning
Unsupervised Learning is another subset of machine learning. Unsupervised learning involves algorithms that learn independently without labels or prior training. These models are provided with raw, unlabeled data and must independently identify patterns, similarities, and differences. One type of unsupervised learning is clustering, where the machine will try to cluster the data it receives and create groups.
Let’s say you are an e-commerce store that wants to understand your customers’ behavior better and improve your marketing strategies. However, you don’t have labeled data that categorizes your customers into predefined segments. This is where unsupervised learning, specifically clustering, can be helpful.
You start by collecting raw data on your customers, including information like:
- Purchase History: Total number of purchases, frequency, average purchase value. ( for example, from Woocommerce)
- Browsing Behavior: Pages visited, time spent on the website, products viewed. ( from Google Analytics)
- Demographics: Age, gender, location, income level. (from Google Analytics or surveys)
This data is unlabeled, meaning there’s no predefined category or segment for each customer.
Applying a K-Means Clustering Algorithm
You apply a clustering algorithm, such as K-means clustering, to the dataset. The algorithm scans the data and tries to identify patterns, similarities, and differences among the customers. Based on these patterns, the algorithm groups customers into clusters. Each cluster represents a group of customers with similar characteristics.
Understanding the Clusters
After running the clustering algorithm, you might find that your customers are grouped into the following clusters:
Cluster 1: High-frequency buyers who spend much per transaction but visit the site less frequently. Customers in the first cluster should receive exclusive discounts or loyalty rewards.
Cluster 2: Regular visitors with a moderate purchase frequency and average spending per transaction. Customers in this cluster may purchase more if they receive more personalized recommendations.
Cluster 3: Occasional visitors who browse a lot but purchase infrequently and have a lower average spend. Target customers with ads or promotional emails, such as a cart reminder email campaign.
This is just an example; the clusters will depend on the e-commerce platform’s data, and the marketing strategies will depend on the budget and the store’s specific situation.
1.3 Reinforcement Learning
Reinforcement learning involves an agent interacting with an environment over time. At each step, the agent observes the environment, takes action, and receives feedback as a reward. The process then repeats: the agent makes another observation, takes another action, and gets another reward. The agent’s behavior is guided by a policy, a rule, or a function that decides what action to take based on the current observation. The main goal of reinforcement learning is to develop effective policies that produce the best possible rewards.
For example, reinforcement learning works in a self-driving car by having the car, or agent, interact with its environment—the road, other vehicles, pedestrians, and traffic signals. The car constantly receives observations from its sensors, like cameras and radar, which provide information about its surroundings. Based on these observations, the car decides what action to take, such as turning, braking, or accelerating.
After each action, the car receives feedback (reward) indicating how well it performed. For example, safely stopping at a red light might earn a positive reward, while getting too close to another vehicle could result in a negative reward. The car uses this feedback to learn over time, adjusting its strategy or policy to make better decisions in the future. The vehicle aims to maximize positive rewards, leading to safe and efficient driving.
Example: A self-driving vehicle stopping at a red light.
The self-driving car is the agent that needs to make decisions. The environment is the intersection where the traffic light has turned red, signaling the car to stop. The vehicle decides to stop as it approaches the intersection because the light is red. This is the action it takes. The car observes the environment’s state, including the red light, the car’s position relative to the intersection, and possibly other cars stopping. The vehicle receives a positive reward for stopping because it follows the traffic rules and avoids a potential accident. The car’s policy guides it to stop whenever it encounters a red light at an intersection. Over time, it learns to stop correctly and consistently at red lights.
Because driving involves many unpredictable, complex scenarios, cars cannot completely self-drive without making mistakes. According to the Society of Automobile Engineers, the highest level of autonomous vehicles for sale in the USA is level 3 cars, which can self-drive. Still, humans must be prepared to take over when necessary. Only the Honda Legend, the Mercedes EQS, and the S-class are approved for level 3 autonomous driving in the USA.
To learn more about Mercedes’ self-driving technologies, watch this video: