Foundations: Introduction, Intelligent Agents

Search & Problem Solving: Classical Search, Beyond Search, Adversarial Search, Constraint Satisfaction

Knowledge & Logic: Logical Agents, First-Order Logic, Inference, Knowledge Representation

Planning & Decision Making: Classical Planning, Acting in the Real World, Uncertainty, Probabilistic Reasoning, Simple & Complex Decisions

Learning: From Examples, Knowledge in Learning, Probabilistic Models, Deep Learning, Reinforcement Learning

Natural Language & Perception: NLP for Communication, Perception

Robotics: Planning, Sensing, and Acting in Real Environments

Philosophy & Future: Philosophical Foundations, The Present & Future of AI

ML Basics: Landscape, End-to-End Projects, Classification

Modeling: Training Models, SVMs, Decision Trees, Ensembles & Random Forests

Advanced Techniques: Dimensionality Reduction, Unsupervised Learning

Neural Networks: ANN with Keras, Deep Nets Training, TensorFlow Custom Models

Deep Learning: Data Preprocessing, CNNs for Vision, RNNs for Sequences

NLP: RNNs, Attention, Transformers, Pretraining

Generative AI: Autoencoders, GANs, Representation Learning

Other Key Areas: Reinforcement Learning, Latest ML Trends

Foundations: What Is Generative Deep Learning?, TensorFlow & Keras Basics

GANs (Generative Adversarial Networks): Intro to GANs, DCGANs, Improved Training, Conditional GANs, Pix2Pix, CycleGAN, BigGAN, Progressive Growing, StyleGAN & StyleGAN2

VAEs & Autoencoders: Variational Autoencoders, Collaborative Filtering Applications

Transformers & Large Models: Transformers, GPT & GPT-2, BERT, Multimodal AI, MuseNet & Music Generation

Diffusion Models: DDPMs (Denoising Diffusion Probabilistic Models)

Applications: Generative AI in Science & Real-World Use Cases

Future Outlook: Trends & The Road Ahead

Foundations: Introduction, Linear Algebra, Probability & Information Theory, Numerical Computation, ML Basics

Core Models: Feedforward Networks, Regularization, Optimization for Training

Architectures: Convolutional Networks (CNNs), Sequence Models (RNNs & Recursive Nets)

Methodology & Applications: Practical Strategies, Real-World Use Cases

Representation Learning: Autoencoders, Linear Factor Models, Structured Probabilistic Models

Generative Models: Basic & Advanced Deep Generative Models, Monte Carlo Methods, Approximate Inference, Partition Function Challenges

Wrap-Up: Conclusions & Outlook