Artificial Intelligence & Deep Learning

Academic Year 2025-2026

Foundations of machine learning (ML), artificial intelligence (AI), and deep neural networks through comprehensive courses, interactive visualizations, and hands-on practical sessions.
IN IN IN H H OUT

Course Overview

Syllabus

Course Content & Structure

Complete syllabus outlining all topics covered in this course.

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Course Materials

Introduction

Introduction

Comprehensive overview of learning objectives, assessment criteria, and detailed schedule of lectures and practical sessions throughout the academic year.

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Overview

Artificial Intelligence

Foundational principles of AI: knowledge representation, reasoning systems, and intelligent agents.

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Core Topics

Machine Learning

Introduction to biological neurons and the core algorithms of the Perceptron, multilayer Perceptron, and artificial neural networks.

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Advanced

Deep Learning

Introduction to deep learning concepts and neural architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

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Specialized Topics

Symbolic AI

Introduction to logic-based systems, symbolic reasoning, and applications in natural language understanding.

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Historical Context

History of AI

The evolution of AI methodologies, major milestones, classification algorithms, and their evaluation.

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Interactive Visualizations

Basic

Perceptron

Interactive visualization of a single-layer perceptron, the foundational model of neural networks.

Explore Perceptron
Intermediate

Multi-Layer Perceptron

Visualization of how multiple perceptron layers collaborate to solve non-linear classification tasks.

Explore MLP
Advanced

Deep Neural Network

Architecture and data flow through deep neural networks with multiple hidden layers.

Explore DNN

Practical Sessions

GitHub Repository

IA-DeepLearning Repository

Comprehensive repository containing Jupyter notebooks, code samples, and project templates with bilingual documentation.

Access on GitHub