Concept library
638 concepts across 9 domains and 52 tracks. Each track is a coherent sequence — read it top to bottom or dip in wherever the gap is.
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Trees And Ensembles 4 concepts · 31 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.
Bayesian Methods 4 concepts · 31 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.
Classical Ir 4 concepts · 30 cards
- 01 BM25 and Term Frequency Saturation Why a term appearing twenty times should not score ten times higher than one appearing twice, how BM25 encodes that as a saturating function, and what its two parameters actually control.
- 02 Inverted Indexes and Postings Lists The data structure that makes text search sublinear in corpus size, why postings are stored as sorted document IDs, and how gap encoding turns a list of integers into a few bits each.
- 03 Dynamic Pruning with WAND and Block-Max Retrieving the exact top-k without scoring most of the candidates, by maintaining an upper bound on what each document could score and skipping everything that cannot beat the current threshold.
- 04 Learned Sparse Retrieval Using a language model to assign weights over the vocabulary, including terms not present in the text, so semantic matching runs on an inverted index instead of a vector index.
Experimentation Ab Testing 5 concepts · 32 cards
- 01 Sample Ratio Mismatch When the observed traffic split differs from the intended one, the randomisation is compromised and the effect estimate should be discarded rather than adjusted, because the cause is almost always a mechanism that also biases the metric.
- 02 Statistical Power and the Minimum Detectable Effect Sample size is decided by the smallest effect worth detecting, not by convention, and the fourth-power relationship between effect size and required traffic is why most product experiments are underpowered.
- 03 Interference and Network Effects in Experiments When one unit's treatment affects another unit's outcome, individual randomisation measures a quantity that is neither the treatment effect nor zero, and the standard designs trade bias against a large loss of power.
- 04 Peeking and Sequential Testing Fixed-sample p-values assume the sample size was chosen in advance, so continuously monitoring a dashboard and stopping at significance can inflate the false-positive rate several-fold.
- 05 Variance Reduction with CUPED and Stratification Regressing out pre-experiment behaviour removes variance that has nothing to do with the treatment, buying sensitivity without extra traffic, and the size of the gain is set by one correlation.
Policy Learning And Ope 4 concepts · 30 cards
- 01 Doubly Robust Estimation Combining an outcome model with a propensity model so that the estimate stays consistent if either one is correct, and why the modern version adds cross-fitting to make that guarantee usable.
- 02 Heterogeneous Treatment Effects and Uplift Estimating who benefits rather than whether the average benefits, and why the target quantity is never observed for any individual, which breaks every standard model-selection habit.
- 03 Inverse Propensity Scoring for Off-Policy Evaluation Estimating how a new policy would have performed using only logs from an old one, by reweighting each logged decision by how much more likely the new policy was to make it.
- 04 Off-Policy Evaluation for Rankers and LLM Systems Applying counterfactual estimation where the action is a ranked list or a generated response, where the action space is effectively unbounded and the logging policy was never stochastic.
Learning To Rank 4 concepts · 28 cards
- 01 Pointwise, Pairwise and Listwise Objectives Ranking is not regression, and the three families of learning-to-rank losses differ in how much of the ranking structure they put inside the objective rather than leaving to a sort.
- 02 Counterfactual Learning to Rank Training a ranker on logged clicks while correcting for the bias in how those clicks were generated, which turns a biased log into an unbiased estimate of a ranking objective.
- 03 LambdaRank and LambdaMART The trick of defining a gradient without ever defining a loss, which lets gradient boosting optimise a discontinuous ranking metric directly, and why the result dominated learning to rank for a decade.
- 04 Position Bias and the Examination Hypothesis Clicks measure relevance multiplied by the chance the user looked, so training on raw clicks teaches a ranker to reproduce whatever ranking generated the logs.
Query Understanding 4 concepts · 28 cards
- 01 Query Intent and Taxonomies The same string can be three different requests, and classifying which one determines whether the right answer is a document, an entity, an action, or a generated response.
- 02 Spelling Correction and Query Segmentation Fixing what the user typed before matching it, where the hard part is not generating candidates but deciding whether the original was wrong at all.
- 03 Conversational Query Rewriting Turning a context-dependent follow-up into a self-contained query, which is what lets a stateless retriever serve a stateful conversation.
- 04 Query Expansion and Pseudo-Relevance Feedback Adding terms to a query to bridge vocabulary mismatch, and the drift failure that occurs when the terms are harvested from results that were wrong to begin with.
Causal Foundations 4 concepts · 29 cards
- 01 DAGs, Confounders and Colliders A causal graph turns "which variables should I control for" into a question with a mechanical answer, and shows why conditioning on the wrong variable creates bias rather than removing it.
- 02 Potential Outcomes and the Fundamental Problem A causal effect is a comparison of two outcomes for the same unit, only one of which is ever observed, which makes causal inference a missing-data problem rather than a modelling problem.
- 03 Simpson's Paradox and Choosing an Adjustment Set The same data can show an effect in every subgroup and the opposite effect in aggregate, and the arithmetic cannot tell you which is right; only the causal structure can.
- 04 The Do-Operator and Identification The distinction between conditioning on what you observed and intervening to set a value, and why identification is a question about the graph that must be settled before any estimation.
Supervised Classical 4 concepts · 32 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.
Observational Causal Methods 4 concepts · 29 cards
- 01 Difference-in-Differences and Parallel Trends Using a control group's change over time to estimate what the treated group's change would have been, and the untestable assumption that carries the entire argument.
- 02 Instrumental Variables Using a source of variation that affects treatment but has no other path to the outcome, which recovers a causal effect despite unmeasured confounding, for a subpopulation you cannot identify.
- 03 Propensity Scores and Matching Reducing a high-dimensional covariate vector to a single probability of treatment, which makes balancing tractable but does nothing about the confounders you did not measure.
- 04 Regression Discontinuity and Synthetic Control Two designs that manufacture a credible counterfactual, one from an arbitrary threshold in an assignment rule and one from a weighted combination of untreated units.
Search Evaluation 4 concepts · 27 cards
- 01 Test Collections, Pooling and Judgment Bias The Cranfield paradigm made retrieval a measurable science, and the pooling shortcut that makes it affordable quietly penalises any system unlike the ones that built the pool.
- 02 nDCG, MRR and Graded Relevance The main ranking metrics differ in what they assume about the user, and choosing one is choosing a model of how far someone reads and what they are looking for.
- 03 Interleaving and Online Evaluation Mixing two rankers' results into a single list and attributing clicks gives a within-user paired comparison that detects differences far faster than an A/B test on the same traffic.
- 04 Why Offline Gains Vanish Online The recurring experience that an offline nDCG improvement produces no measurable online effect, and the four distinct mechanisms that cause it.
Recommender Systems 3 concepts · 23 cards
- 01 Matrix Factorisation and Implicit Feedback Learning low-rank user and item vectors from a sparse interaction matrix, and why the shift from ratings to clicks changes the loss, the negatives and the meaning of the output.
- 02 Feedback Loops and Filter Bubbles A recommender trained on data it generated is optimising against its own past choices, which narrows what users see and makes offline evaluation systematically agree with the incumbent.
- 03 Two-Tower Retrieval and Candidate Generation Splitting the model so that item representations can be precomputed and searched with approximate nearest neighbours, which is what makes recommending from a hundred-million-item catalogue possible at all.
Unsupervised Learning 4 concepts · 31 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.
Statistical Inference 4 concepts · 32 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.
Feature Engineering 4 concepts · 31 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.
Time Series Foundations 4 concepts · 28 cards
- 01 Seasonality and Decomposition Splitting a series into trend, seasonal and remainder components, and the choice between additive and multiplicative structure that determines whether the seasonal pattern grows with the level.
- 02 Autocorrelation and ARIMA Reading the autocorrelation and partial autocorrelation functions to identify how much of a series is explained by its own past, and what the AR, I and MA components each represent.
- 03 Backtesting and Temporal Validation Random cross-validation on time series lets a model learn from the future, and the alternatives all trade honesty against how much of the data can be used.
- 04 Stationarity and Differencing Almost every time series method assumes the statistical properties do not change over time, and the transformations that enforce that assumption also change what the model is predicting.