Concept library
638 concepts across 13 domains and 52 tracks. Each track is a coherent sequence — read it top to bottom or dip in wherever the gap is.
All domains
01Foundations
02Transformer Internals
03Training & Fine-Tuning
04Reinforcement Learning
05Inference, Systems & Hardware
06Applied LLM Engineering
07Reasoning, Evaluation & Safety
08Multimodal & Applications
09Classical ML & Statistical Learning
10Causal Inference & Experimentation
11Time Series & Forecasting
12Graphs, Recommenders & Structured Data
15Search & Information Retrieval
09
Classical ML & Statistical Learning
The statistics and non-neural models that still decide most production predictions.
6tracks
24concepts
376cards
3.0hreading
Statistical Inference Estimators, likelihood, the bootstrap, hypothesis testing, multiplicity and what a confidence interval really claims. 4 concepts · 64 cards
- 01 Confidence Intervals and Coverage What the 95% in a 95% interval refers to, why the Wald interval for a proportion is badly behaved near zero, and how a confidence interval differs from the credible interval people usually think they are reading.
- 02 Estimators, Bias, Variance and Consistency What makes one estimator better than another, why the mean squared error splits cleanly into bias squared plus variance, and why an unbiased estimator is frequently the wrong thing to want.
- 03 P-Values, Multiplicity and the Garden of Forking Paths What a p-value actually claims, why running twenty tests guarantees a false discovery, and the difference between controlling the family-wise error rate and controlling the false discovery rate.
- 04 The Bootstrap and Resampling Resampling the data you have to simulate the sampling distribution you never observed, why the percentile interval is not always the right one, and the specific statistics for which the bootstrap silently fails.
Classical Supervised Learning Linear and logistic regression, regularisation, margins, kernels, and the geometry underneath them. 4 concepts · 64 cards
- 01 Linear Regression as Projection Least squares is orthogonal projection onto the column space of the design matrix, which explains the normal equations, the meaning of residuals, and why nobody who ships numerical code inverts the matrix.
- 02 Logistic Regression and the Log-Odds Why classification is modelled on the log-odds scale rather than the probability scale, why there is no closed-form solution, and what perfect separation does to the coefficients.
- 03 Ridge, Lasso and Elastic Net Why an $\ell_1$ penalty produces exact zeros while $\ell_2$ only shrinks, what each does to correlated features, and why regularisation is a bias-variance trade rather than a way to fix a bad model.
- 04 Margins, Kernels and the Support Vector Machine How maximising the distance to the nearest point gives a classifier that depends on a handful of examples, and how the dual formulation lets you work in an infinite-dimensional feature space without ever visiting it.
Trees & Ensembles Impurity splitting, bagging, random forests, gradient boosting, and the engineering inside XGBoost and LightGBM. 4 concepts · 62 cards
- 01 Decision Trees and Impurity Splitting How a greedy search over axis-aligned splits builds a piecewise-constant function, why Gini and entropy almost never disagree, and the specific structural biases that make a single tree unstable.
- 02 Bagging and Random Forests Why averaging unstable models reduces variance, why bootstrap sampling alone is not enough, and what the extra feature subsampling in a random forest is actually buying.
- 03 Why Trees Still Beat Deep Nets on Tabular Data The three inductive biases that separate tree ensembles from neural networks on tabular problems, and the specific dataset conditions under which the ordering has been observed to flip.
- 04 Gradient Boosting as Functional Gradient Descent Boosting is gradient descent in function space, where each new tree approximates the negative gradient of the loss, which is what lets a single algorithm fit any differentiable objective.
Unsupervised Learning Clustering, mixture models and EM, PCA and SVD, manifold embeddings, and density estimation. 4 concepts · 62 cards
- 01 K-Means and Its Assumptions Lloyd's algorithm is coordinate descent on a squared-error objective, which explains why it always converges, why it converges to the wrong answer without careful seeding, and the exact cluster shapes it cannot represent.
- 02 PCA, SVD and Whitening Why maximising retained variance and minimising reconstruction error give the same answer, how the SVD computes it without ever forming a covariance matrix, and what whitening destroys.
- 03 UMAP, t-SNE and What They Distort Neighbour embeddings optimise local neighbourhood preservation and nothing else, which makes cluster separation, cluster size and inter-cluster distance in the resulting picture largely uninterpretable.
- 04 Gaussian Mixtures and the EM Algorithm How treating the cluster label as a latent variable turns an intractable likelihood into two closed-form steps, why the likelihood is guaranteed to increase, and why it is unbounded above.
Bayesian Methods Priors and posteriors, MCMC and HMC, variational inference, Gaussian processes and model comparison. 4 concepts · 62 cards
- 01 Priors, Conjugacy and the Posterior How a prior functions as pseudo-data, why conjugate families make the posterior a closed-form update, and why "uninformative" priors are informative on some scale.
- 02 Gaussian Processes Placing a prior over functions rather than parameters, which gives exact posterior uncertainty that grows away from the data, at a cubic cost that determines where they are usable.
- 03 MCMC and Hamiltonian Monte Carlo Why sampling from an unnormalised posterior is possible at all, why random-walk proposals fail in high dimensions, and how using gradient information turns a random walk into directed motion.
- 04 Variational Inference and the ELBO Turning integration into optimisation by fitting a tractable distribution to the posterior, and the specific bias that comes from minimising the reverse KL divergence.
Feature Engineering Encoding, missingness, selection, target leakage, and the train-serve skew that eats offline gains. 4 concepts · 62 cards
- 01 Categorical Encoding Why one-hot encoding breaks down at high cardinality, how target encoding trades that for a leakage risk it must then defend against, and what each choice assumes about unseen categories.
- 02 Missing Data Mechanisms Whether imputation is safe depends on why the value is absent, and the three-way distinction between MCAR, MAR and MNAR decides which methods are valid and which quietly bias the result.
- 03 Target Leakage The failure mode where a feature encodes information unavailable at prediction time, why cross-validation cannot detect it, and the three structural forms it takes.
- 04 Train-Serve Skew The gap between how a feature is computed in a training pipeline and how it is computed in a serving path, which silently degrades a model that was never wrong in offline evaluation.