Academic Year 2026-2027
Complete syllabus outlining all topics covered in this course.
View Course PlanComprehensive overview of learning objectives, assessment criteria, and detailed schedule of lectures and practical sessions throughout the academic year.
View Course (French)Foundational principles of AI: knowledge representation, reasoning systems, and intelligent agents.
View Course (French)Introduction to biological neurons and the core algorithms of the Perceptron, multilayer Perceptron, and artificial neural networks.
View Course (French)Introduction to deep learning concepts and neural architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
View Course (French)Introduction to logic-based systems, symbolic reasoning, and applications in natural language understanding.
View Course (French)The evolution of AI methodologies, major milestones, classification algorithms, and their evaluation.
View Course (French)Interactive visualization of a single-layer perceptron, the foundational model of neural networks.
Explore PerceptronVisualization of how multiple perceptron layers collaborate to solve non-linear classification tasks.
Explore MLPArchitecture and data flow through deep neural networks with multiple hidden layers.
Explore DNNA comprehensive workflow framework for building, training, and deploying deep learning models across any domain.
Explore Deep Learning PipelineDraw a shape and follow it through a trained CNN, then explore convolution, activation, pooling, receptive field, dropout, batch normalisation and softmax interactively.
Explore CNN and LayersComprehensive repository containing Jupyter notebooks, code samples, and project templates with bilingual documentation.
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