Interactive ML Prof. Gennady Roshchupkin  ↗
Interactive health ML

See how models really work.

Explore complex machine-learning ideas through interactive explanations, visual experiments, and health-focused examples.

Explore the roadmap Test your knowledge
40+ visual lessons65+ quiz questionsHealth ML examples
Biomedical ML lifecycle Evidence before deployment
  1. 1
    Define the cohort and outcome EHR, imaging, genomics or sensors · eligibility · time zero
  2. 2
    Preprocess without leakage Impute, encode and scale inside each training partition
  3. 3
    Develop and tune the model Compare candidates · regularise · use nested resampling
  4. 4
    Validate performance and transportability Internal + external validation · calibration · subgroups
  5. 5
    Assess utility, impact and monitoring Thresholds · net benefit · prospective impact · model drift
Explore the library

Learn by concept.

Choose a topic and move at your own pace. Each lesson pairs concise theory with an interactive or visual explanation.

🧭 Learning Navigation 💡 Start here! Use the roadmap to plan your learning journey, then test your knowledge with the quiz.

Interactive Learning Roadmap
Navigate your ML journey with an interactive mindmap organized by topics, difficulty levels, and learning paths
ML Knowledge Quiz
Test your knowledge with 65+ interactive questions covering ML basics, optimization, validation, and applications

🤖 Core Machine Learning Concepts

ML Algorithms Overview
Introduction to machine learning algorithms and their applications
ML Algorithms Comprehensive Guide
Complete guide to machine learning algorithms with examples and practical applications
ML Algorithms Mathematical Guide
Mathematical foundations and detailed formulations of machine learning algorithms
Geometric Interpretation of ML
Visual understanding of machine learning algorithms
ML Algorithm Animations
Step-by-step visual lab for linear regression, clustering, trees, neural networks, random forests, and logistic regression
Cost Functions & Loss Visualization
Understanding optimization through cost functions
Optimization Algorithms
Different approaches to model optimization
ML Algorithm Selection Flowchart
Interactive guide to choosing the right ML algorithm

📊 Data & Mathematical Foundations

Data as a Matrix
Understanding how data is structured as matrices
Non-Invertible Matrices
When matrices cannot be inverted and why it matters

🧪 Data Preparation & Pipelines

Missing Data, Imputation & Safe ML Pipelines
Practical guide to preprocessing health datasets without leakage, from missing values to train-only pipelines
Label Quality & Outcome Definition in EHR Data
Student-friendly guide to defining outcomes, spotting noisy proxy labels, and avoiding label leakage in health ML

📈 Dimensionality Reduction

PCA Guide
Comprehensive guide to Principal Component Analysis with examples

📊 Model Evaluation & Validation

Train, Validation, Test Splits & Data Leakage
Foundational guide to safe data splitting, leakage prevention, and realistic evaluation in health ML
External Validation & Transportability
Healthcare guide to testing whether models generalize across hospitals, populations, and time periods
Deployment, Monitoring & Model Drift
Health ML guide to post-deployment surveillance, drift detection, alert fatigue, rollback, and recalibration decisions
Clinical Workflow Integration & Human-AI Decision Support
Healthcare guide to where predictions enter care pathways, who acts on them, alert burden, oversight, and safe workflow design
Prospective Validation, Impact Studies & Randomized Evaluation
Health ML guide to silent trials, pilot rollouts, pragmatic comparisons, and how to test whether a model changes care safely
Bias-Variance Tradeoff
Understanding model complexity and generalization
Bias-Variance Applications
Applied examples of bias-variance tradeoff
ROC Curves & Performance Metrics
Evaluating classification model performance
Precision, Recall & Class Imbalance
Health ML guide to rare outcomes, alert quality, prevalence shift, and precision-recall tradeoffs
Calibration, Thresholds & Clinical Decisions
Student-friendly guide to probability reliability, threshold selection, and action tradeoffs in health ML
Survival Analysis & Competing Risks
Health ML guide to time-to-event outcomes, censoring, survival curves, and competing clinical events
Model Interpretability & Explainable AI
Healthcare-focused guide to global and local explanations, feature effects, and safe model reporting
ML Evaluation Metrics Table
Comprehensive overview of machine learning evaluation metrics

🔄 Cross-Validation Techniques

K-Fold Cross-Validation
Interactive visualization of k-fold validation
Nested Cross-Validation
Advanced validation for hyperparameter tuning
Training Process Visualization
Visual guide to the training and validation process

⚙️ Hyperparameter Optimization

Hyperparameter Tuning Guide
Comprehensive guide to optimizing model parameters

🎯 Ensemble Learning

Ensemble Methods Interactive
Interactive exploration of ensemble learning techniques

🧠 Neural Networks

NN vs Classical ML Training
Training approaches comparison between neural networks and classical ML
Neural Network Training Slides
Comprehensive guide to neural network training process
Parameter Growth in Neural Networks
Understanding how parameters scale with network size
Neural Network Issues & Challenges
Common problems and solutions in neural network training
Backpropagation Learning Process
Interactive visualization of how neural networks learn through backpropagation
Autoencoder Tutorial
Comprehensive guide to autoencoders with interactive examples and healthcare applications
Autoencoder Animations
Interactive visualizations of different autoencoder architectures with mathematical details and training guidelines
Biological vs Artificial Neural Networks
Interactive comparison between biological and artificial neurons with live calculator and activation function exploration

⚖️ Bias & Fairness in ML

Understanding Bias in ML
Comprehensive guide to bias types, detection, and prevention in machine learning
Causal Inference & Confounding
Healthcare-focused guide to separating association, prediction, and causal effects with DAGs and confounding examples

🧠 Advanced Topics

Federated Learning
Privacy-preserving collaborative AI training with interactive demonstrations
Neural Network Architecture
Understanding different neural network architectures and designs
Needs Fix

© 2025 Interactive ML Learning Platform | Prof. Gennady Roshchupkin

Interactive slides designed for enhanced learning experience

🆘 Help & Support

🚀 Getting Started

Welcome to the Interactive ML Learning Platform! Here's how to make the most of your learning experience:

  • Start with the Roadmap: Use the interactive roadmap to plan your learning journey
  • Take the Quiz: Test your current knowledge to identify areas for improvement
  • Follow Learning Paths: Topics are organized by difficulty level (Beginner → Intermediate → Advanced)
  • Interactive Elements: Click, hover, and explore to engage with the content

📚 Navigation Tips

  • Topic Categories: Content is organized into logical categories for easy browsing
  • Status Badges: Look for "Needs Fix" badges on content under development
  • Mobile Friendly: All content is optimized for mobile devices
  • Search Function: Use the roadmap search to find specific topics quickly

🎯 Learning Recommendations

💡 Pro Tip: Start with "Core ML Concepts" if you're new to machine learning, or jump to "Advanced Topics" if you have ML experience and want to explore deeper concepts.
  • Beginners: Start with Mathematical Foundations → Core ML Concepts → Data Preparation & Pipelines → Data Splitting & Leakage → Model Evaluation
  • Intermediate: Focus on Model Evaluation → Calibration & Thresholds → Precision, Recall & Imbalance → Cross-Validation
  • Advanced: Explore Neural Networks → Advanced Topics → Specialized Applications

🔧 Technical Support

If you encounter any issues:

  • Browser Requirements: Use a modern browser (Chrome, Firefox, Safari, Edge) with JavaScript enabled
  • Mobile Issues: Try rotating your device or using landscape mode for better viewing
  • Interactive Elements: Some animations may take a moment to load on slower connections
  • Content Updates: Check back regularly as new content is being added

📞 Contact & Feedback

For questions, suggestions, or to report issues:

  • Platform Creator: Prof. Gennady Roshchupkin
  • Platform: Interactive ML Learning Platform
  • Support: This is an educational resource - please be patient with any technical issues
🎓 Learning Goal: This platform is designed to make complex ML concepts accessible through interactive visualizations. Take your time, experiment with the tools, and don't hesitate to revisit topics as your understanding grows.