Theory

Mathematical Foundations

• Quantum Mechanics - Schrödinger and Dirac equations
• Linear Algebra - Vector spaces, linear transformations, matrices, eigenvalues
• Mathematical Analysis - Calculus, differential equations, Fourier analysis
• Mathematical Statistics - Probability distributions, Bayes theorem, information entropy

Machine Learning Paradigms

• Learning Paradigms - Supervised, unsupervised, self-supervised, and reinforcement learning

Neural Network Architectures

• Multilayer Perceptron - Foundations of neural networks: forward pass, backpropagation, optimization
• Neural Architectures - CNNs, RNNs, LSTMs, and ResNets
• Transformers - Attention mechanisms, self-attention, and the Transformer architecture

Advanced Topics

• Explainable AI - Interpretability and explanation methods for AI systems