Computer Engineering is a field that has never stood still. From simple encoders and decoders to mainframes, personal computers, the Y2K frenzy, and the Internet boom — every milestone marked a leap forward. Each wave gave rise to new subfields or even entirely new disciplines.

We’ve always understood that data matters, but few predicted that data engineering, analytics, and modeling would become the specialized, critical engines powering every intelligent system today. The recent explosion of technologies like Large Language Models (LLMs) has pushed the spotlight squarely onto Artificial Intelligence (AI). While other tech fields thrive, the curiosity and excitement around AI have turned it into the ultimate frontier—drawing every engineer, researcher, and enthusiast to explore its infinite possibilities.

From classical models to deep learning – evolution of Machine Learning

Before we dive into the evolution of models, let’s precisely define the relationship between the three biggest buzzwords in tech: Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). While often used interchangeably, they represent a clear, nested hierarchy.

• Artificial Intelligence (AI): The Overarching Goal. AI is the broadest concept. It refers to any system or technique that mimics human cognitive functions, such as problem-solving, learning, or decision-making. This includes systems based on simple, hard-coded rules (like an old-school expert system) and sophisticated modern algorithms (like a generative chatbot). It is the engineering discipline dedicated to creating intelligent machines.
• Machine Learning (ML): The Data-Driven Approach. ML is a subset of AI. The core distinction is that ML focuses on algorithms that can learn directly from data without being explicitly programmed. Instead of following fixed rules, an ML model observes patterns, trends, and relationships in large datasets to make predictions or decisions. This approach is the primary method used today to build scalable, data-driven solutions.
• Deep Learning (DL): The Advanced Tool for Complexity. DL is an advanced subset of ML. It utilizes multi-layered neural networks (hence “deep”) to process vast amounts of data. This architecture allows the model to automatically extract and learn complex, high-level features directly from raw inputs like pixels or speech waveforms. DL is used for highly intricate tasks where traditional ML and statistical methods hit a performance ceiling.

The Golden Rule: All Machine Learning solutions are considered Artificial Intelligence, but not all AI systems use Machine Learning.
[Post your views here, if you have have a differing opinion on the above]

In essence:

AI is the goal,
ML is the approach,
DL is the tool that scales it to complexity.

This fundamental relationship has driven decades of innovation. The concept of the neural network, which powers DL, was first introduced way back in 1943. As you can see from the timeline below, the evolution of ML models spans this long, rich history:

Types of Models

At a very broad level, we can divide them into two categories:

1. Traditional ML Models
   • Linear/Logistic Regression
   • Decision Trees and Ensembles
   • Support Vector Machines(SVM)
   • Classical Models : Naïve Bayes (for text/spam filtering), k‑means clustering (for segmentation), PCA (for dimensionality reduction), and simple neural networks are also used depending on data type.
2. Deep Learning Models
   • CNNs ( Convolutional Neural Networks)
   • RNN/LSTM/GRU(Recurrent Networks)
   • Transformers/ LLMs
   • Generative Models(Generative Adversarial Networks(GAN),Variational AutoEncoders(VAEs)

In general, enterprises use deep learning for complex, unstructured tasks (e.g. virtual assistants or fraud detection) and stick to traditional ML for well-understood, structured problems

There is a series of papers that will follow detailing on each of the traditional and deep learning models. Stay put and keep learning!