Dr. Joseph Muguro
Researcher and Engineer
A researcher and engineer interested in bioengineering, machine learning, robotics, and computational modeling of engineering systems.
About Dr. Joseph Muguro
Focus
📚 Education Contributions
From Training to Deployment
Deploy ML models three ways: as a Python script, as a Flask REST API, and as C arrays for microcontrollers. Learn model monitoring, data drift detection, and the complete ML lifecycle from training to production retraining.
Published: March 9, 2026
Working with Real Sensor Data
Handle the messiness of real-world sensor data: missing values, outliers, drift, and noise. Engineer features like rolling averages, rate of change, and FFT peaks. Build a predictive maintenance model that detects pump failures 24 hours early.
Published: March 8, 2026
Practical ML with Scikit-Learn
Master the complete ML workflow using Scikit-Learn: data loading, preprocessing pipelines, cross-validation, hyperparameter tuning with GridSearchCV, model comparison, and persistence. Build a reusable template for any ML project.
Published: March 7, 2026
Neural Networks from Scratch
Build a neural network in pure NumPy. Understand forward passes, backpropagation, and training loops by implementing every step yourself. Solve the XOR problem and classify sensor readings into normal, warning, and critical states.
Published: March 6, 2026
How Models Learn: Gradient Descent
Implement gradient descent from scratch in NumPy. Understand loss functions, gradients, learning rate, and why gradient descent is the engine behind all modern machine learning. Complete, runnable Python code.
Published: March 5, 2026
Decision Trees and Random Forests
Predict equipment failure from vibration, temperature, and operating hours using decision trees and random forests. Visualize tree rules, interpret feature importance, and compare single trees to ensemble methods.
Published: March 4, 2026
Classification: Yes or No Decisions
Detect defective sensor boards from test measurements using logistic regression. Learn confusion matrices, precision, recall, ROC curves, and how to handle imbalanced classes with complete, runnable Python code.
Published: March 3, 2026
Linear Regression and Prediction
Predict indoor temperature from sensor data using scikit-learn. Build the full ML pipeline: synthetic data generation, feature scaling, train/test split, evaluation metrics (MSE, MAE, R-squared), and residual analysis.
Published: March 2, 2026
What Machine Learning Actually Is
Machine learning is curve fitting, generalized. Start with np.polyfit, see overfitting with your own eyes, learn the train/test split, and understand the bias-variance tradeoff with complete, runnable Python code.
Published: March 1, 2026
Thinking Like a Scientist-Engineer
Synthesize scientific rigor with engineering pragmatism. Learn when to be precise, when to be fast, and how to build a personal practice that combines the best of both disciplines.
Published: March 9, 2026
Technology, Society, and Unintended Consequences
Examine how every technology produces effects beyond its intended purpose, from the automobile's reshaping of cities to social media's transformation of public discourse.
Published: March 8, 2026
Ethics and Responsibility in Engineering
Examine the moral obligations that come with engineering decisions, through real case studies of catastrophic failures and the professional frameworks designed to prevent them.
Published: March 7, 2026
Uncertainty and the Limits of Knowledge
Explore what engineers can know precisely, what remains fundamentally uncertain, and how to design responsibly when complete knowledge is impossible.
Published: March 6, 2026
Models, Maps, and Reality
All models are wrong, but some are useful. When SPICE simulations succeed, when financial models catastrophically fail, and how to use engineering models wisely by knowing their assumptions, testing their boundaries, and validating against reality.
Published: March 5, 2026
Paradigm Shifts: How Engineering Knowledge Evolves
Kuhn's model of scientific revolutions applied to engineering: vacuum tubes to transistors, CISC to RISC, C to Rust. Why paradigm shifts are resisted, how to recognize them, and what Lakatos adds about progressive vs degenerating research programs.
Published: March 4, 2026
Falsifiability: Testing to Fail
Popper's key insight applied to engineering: the goal of testing is to discover how your design fails, not to confirm it works. Confirmation bias, negative testing, the Challenger disaster, and test-driven development as institutionalized falsification.
Published: March 3, 2026
The Scientific Method in Engineering Practice
The textbook scientific method versus the messy reality. Debugging is hypothesis testing, design reviews are peer review, test plans are experiments. Case study: Edison's systematic approach to invention.
Published: March 2, 2026
What Makes Something Scientific?
The demarcation problem: how do you tell science from non-science? Popper's falsifiability criterion, pseudoscience red flags, and the cold fusion story. Learn to evaluate engineering claims, datasheets, and research papers critically.
Published: March 1, 2026