Articles
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AI coding tools should reach beyond the editor
AI-assisted coding accelerates one pillar of development work — writing code. The real leverage is an assistant that works across all three pillars, project management and infrastructure included, closing the whole loop instead of one third of it.
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Enabling data-driven results
Integrating decision support systems into business processes — the building blocks of descriptive, predictive, and prescriptive systems, and why results happen in processes, not in the systems themselves.
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Artificial intelligence: a concise conceptual introduction
A conceptual map of artificial intelligence — from the definition of intelligence to the five abstraction layers of every machine learning solution.
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Multiple linear regression
Linear regression with several features in matrix form: the closed-form least-squares estimator derived and computed, the sampling distribution of the coefficients, and a from-scratch look at LASSO, ridge, and elastic-net regularization.
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Simple linear regression
Fitting a line from scratch: the model and its assumptions, least-squares estimates derived by hand and by optimizer, the sampling distributions of the coefficients, a confidence band for the mean response, and R².
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Single-parameter frequentist inference
Three estimators for the mean of a normal sample — sample mean, least squares, and maximum likelihood — derived from scratch and shown to coincide, then confidence intervals checked against the truth.
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The law of large numbers
Why the average of many trials settles on the expected value — shown with coin and dice simulations in NumPy, then the weak law stated and proved via Chebyshev's inequality.
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Markov chains
A from-scratch look at Markov chains with NumPy — the Markov property, transition matrices, and how any starting distribution converges to the same stationary distribution.