Machine Learning Learning Scenarios & Paradigms
Deep dives into Unsupervised Learning, Reinforcement Learning, Meta-Learning, Online Learning architectures with Python coding snippets.
A technical breakdown of heuristic-based unsupervised learning, RL reward structures, and the agility of online/meta-learning systems.
Categories
- Unsupervised Learning:
Discovering latent structures within unlabeled datasets through clustering and density estimation. - Reinforcement Learning:
Optimizing agent decision-making policies through feedback loops and Markov Decision Processes. - Meta Learning:
Engineering learning to learn frameworks with minimal data overhead. - Online Learning:
Implementing incremental algorithms that ingest data streams in real-time. - Supervised Learning:
Train the mode on labeled data
Unsupervised Learning
Discovering latent structures and identifying statistical outliers within unlabeled datasets through clustering and density estimation.
Beyond Labels: Implementing Unsupervised Anomaly Detection with Isolation Forest and LightGBM
Explore practical implementation of anomaly detection scheme with IsolationForest and automated feedback loops
A practical deep dive into detecting irregular data patterns using unsupervised machine learning. This guide covers LLM Fine-tuning behind Isolation Forest, human-in-the-loop evaluation, and a full simulation of a fraud detection lifecycle using LightGBM.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
Beyond K-Means: A Deep Dive into Gaussian Mixture Models and the EM Algorithm
A deep dive into the core concepts of unsupervised clustering with practical application on customer data segmentation
Unpack the probabilistic mechanics of Gaussian Mixture Models (GMM). From Jensen’s Inequality and log-likelihood maximization to soft assignment of latent variables, explore why the EM algorithm is the gold standard for modeling complex, non-spherical data distributions.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
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Reinforcement Learning
Optimizing agent decision-making policies through reward-based environmental feedback loops and Markov Decision Processes.
Deep Reinforcement Learning for Self-Evolving AI
Building self-learning systems
Deep Reinforcement Learning (DRL) is a key component in AI, enabling algorithms to learn and adaptively improve through continuous feedback.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
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Meta Learning
Engineering learning to learn frameworks that enable models to rapidly generalize to new tasks with minimal data overhead.
Scaling Generalization: Automating Flexible AI with Meta-Learning and NAS
Explore how adaptable neural networks handle few-shot learning
Standard AI excels at specialization but fails at adaptation. This article explores the powerful synergy between Neural Architecture Search (NAS) and Meta-Learning, demonstrating how to automate the design of architectures specifically optimized for rapid learning. We walk through a practical implementation using MAML and RL-based controllers to solve few-shot animal classification tasks, proving that AI can learn to learn.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
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Online Learning
Implementing incremental algorithms that ingest data streams in real-time, allowing for continuous model adaptation without retraining from scratch.
Online Learning in Action — Building Real-Time Stock Forecasting on Lakehouse
Explore best practices for balancing model stability and adaptation in non-stationary price streams
Online learning is one of the learning scenarios in machine learning where the model is trained sequentially as new data arrives.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
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Supervised Learning
Training the model on labeled data.
Mastering the Bias-Variance Trade-Off: An Empirical Study of VC Dimension and Generalization Bounds
How model complexity and data size impact generalization performance in machine learning
While the bias-variance trade-off is a familiar hurdle in supervised learning, the Vapnik-Chervonenkis (VC) dimension offers the mathematical rigor needed to quantify a model's capacity.
This article evaluates the relationship between the VC dimension, VC bounds, and generalization error through empirical testing on synthetic datasets, demonstrating how theoretical limits translate to real-world model performance.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
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