Specialized Machine Learning Concepts
1. Transfer Learning
Transfer learning reuses knowledge from a pretrained model on one task to accelerate learning on a related target task.
Example: Using a model pretrained on ImageNet to classify medical images.
Benefits:
Less training data required
Faster convergence
Higher accuracy in low-data environments
2. Few-Shot, One-Shot, and Zero-Shot Learning
These techniques deal with extremely limited labeled data.
Few-shot learning
Model learns a task from a few labeled examples.
One-shot learning
Model learns a new class from one example.
Zero-shot learning
Model recognizes new classes without any labeled examples using semantic information (e.g., text descriptions).
Used in: LLMs, vision-language models.
3. Self-Supervised Learning (SSL)
The model generates labels from the data itself using pretext tasks.
Examples:
Masked language modeling (BERT)
Contrastive learning (SimCLR, MoCo)
Predicting missing patches in images (MAE)
SSL significantly reduces labeling cost.
4. Reinforcement Learning (RL)
RL trains an agent to take actions in an environment to maximize cumulative reward.
Key concepts:
Policy (strategy)
Reward (feedback)
Value function (future expected reward)
Exploration vs. exploitation
Applications: robotics, gaming (AlphaGo), LLM optimization (RLHF).
5. Meta-Learning ("Learning to Learn")
Models learn how to adapt quickly to new tasks using prior experience.
Approaches:
Optimization-based (MAML)
Metric-based (Prototypical Networks)
Memory-based (Neural Turing Machines)
6. Federated Learning
Training occurs across distributed devices (e.g., mobile phones) without sending data to a central server.
Important aspects:
Privacy preservation
Model aggregation (FedAvg)
Handling heterogeneous data
Used in: personalized keyboards, medical data collaboration.
7. Graph Machine Learning
Models operate on graph-structured data.
Common methods:
Graph Neural Networks (GNNs)
Graph Convolutional Networks (GCNs)
Graph Attention Networks (GATs)
Applications: recommendation systems, drug discovery, fraud detection.
8. Causal Machine Learning
Identifies cause–effect relationships rather than correlations.
Tools:
Causal graphs
Potential outcomes
Do-calculus
Counterfactual reasoning
Useful for: policy-making, healthcare, root-cause analysis.
9. Contrastive Learning
A self-supervised approach where the model learns by comparing positive and negative pairs.
Example:
Similar items → closer embeddings
Dissimilar items → farther apart
Used in: vision-language models (CLIP), representation learning.
10. Multimodal Learning
Models that process multiple data types simultaneously.
Modalities include:
Text
Image
Audio
Video
Time-series
Sensor data
Examples:
CLIP (image + text)
GPT-4/5 (multimodal input & output)
11. Continual (Lifelong) Learning
Models learn new tasks without forgetting previous ones.
Challenges:
Catastrophic forgetting
Solutions:
Elastic Weight Consolidation (EWC)
Replay buffers
Progressive networks
12. Generative Models
Models that generate new samples similar to training data.
Types:
GANs (Generative Adversarial Networks)
VAEs (Variational Autoencoders)
Diffusion models (Stable Diffusion, DALLE, Imagen)
Applications: synthetic data, art, drug discovery.
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