Advanced Topics in Machine Learning

Generative Models

Generative Adversarial Networks (GANs)

• Basic GAN Architecture
• Conditional GANs
• StyleGAN
• CycleGAN

Variational Autoencoders (VAEs)

• Latent Space Learning
• Conditional VAEs
• Beta-VAE
• Hierarchical VAEs

Flow-based Models

• Normalizing Flows
• RealNVP
• Glow
• Flow++

Transfer Learning

Pre-trained Models

• Fine-tuning
• Feature Extraction
• Domain Adaptation
• Zero-shot Learning

Multi-task Learning

• Hard Parameter Sharing
• Soft Parameter Sharing
• Task Relationships
• Auxiliary Tasks

Meta-Learning

Few-shot Learning

• Metric Learning
• Model-Agnostic Meta-Learning
• Prototypical Networks
• Matching Networks

Learning to Learn

• Meta-Gradients
• Neural Architecture Search
• Hyperparameter Optimization
• AutoML

Reinforcement Learning

Deep Reinforcement Learning

• Deep Q-Networks
• Policy Gradients
• Actor-Critic Methods
• Model-Based RL

Advanced RL Topics

• Multi-Agent RL
• Hierarchical RL
• Inverse RL
• Meta-RL

Explainable AI

Model Interpretation

• Feature Importance
• LIME
• SHAP Values
• Counterfactual Explanations

Visualization Techniques

• Activation Maps
• Attribution Methods
• Decision Trees
• Attention Visualization

Federated Learning

Architecture

• Client-Server Model
• Peer-to-Peer
• Hierarchical
• Cross-Device vs Cross-Silo

Privacy and Security

• Differential Privacy
• Secure Aggregation
• Homomorphic Encryption
• Attack Prevention

Quantum Machine Learning

Quantum Algorithms

• Quantum Neural Networks
• Quantum Support Vector Machines
• Quantum K-means
• Quantum Principal Component Analysis

Hybrid Approaches

• Quantum-Classical Algorithms
• Variational Quantum Circuits
• Quantum Feature Maps
• Quantum Kernels

Edge AI

Edge Computing

• Model Compression
• Quantization
• Pruning
• Knowledge Distillation

Edge Deployment

• Mobile Devices
• IoT Devices
• Edge Servers
• Real-time Processing