AI/ML Models & Algorithms
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1. Artificial Intelligence
Fundamental AI concepts including symbolic AI, machine learning, and deep learning approaches.
Symbolic AI
Expert Systems
Knowledge Graphs
2. Supervised Learning
Models trained on labeled data for regression and classification tasks.
Regression
Classification
Ensemble Methods
3. Unsupervised Learning
Algorithms finding patterns in unlabeled data through clustering and dimensionality reduction.
Clustering
Dimensionality Reduction
Anomaly Detection
4. Semi-Supervised Learning
Methods combining small labeled datasets with abundant unlabeled data.
Self-Training
Co-Training
Label Propagation
5. Reinforcement Learning
Algorithms learning optimal actions through interaction with an environment.
Value-Based
Policy-Based
Actor-Critic
6. Deep Learning
Neural network architectures and models for complex pattern recognition tasks.
CNNs
RNNs
Transformers
7. Transfer Learning
Techniques for adapting pre-trained models to new tasks and domains.
Fine-Tuning
Domain Adaptation
Few-Shot Learning
8. Hybrid & Emerging Techniques
Modern approaches combining multiple paradigms and emerging methodologies.
Neuro-Symbolic AI
Meta-Learning
Federated Learning
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