Learning track 07
ML & Decision Systems
Connect language-model work to statistics, encoders, classical machine learning, forecasting, causality, and bandits.
- Guides
- 4
- Sections
- 28
- Cards
- 110
Suggested sequence
Follow the track or enter where the problem begins.
Every guide is self-contained. The sequence only preserves prerequisite order when one topic depends on another.
- 01Vol. 08
Statistics for Evaluating LLM Systems
Basic concepts of statistics as applied to LLM experiments: quality metrics, variance and confidence intervals, hypothesis testing, A/B tests, LLM-as-judge, evaluation pitfalls and a practical checklist for a valid experiment.
- Sections
- 7
- Cards
- 24
- 02Vol. 10
BERT, Encoders & Non-Generative Models
Taxonomy of transformer architectures: encoder-only, decoder-only, encoder-decoder. How BERT works and why bidirectional is better to understand. BERT family. Fine-tuning patterns. T5 and seq2seq. When a small specialized model beats GPT-4. A practical map for choosing an architecture for a task.
- Sections
- 7
- Cards
- 23
- 03Vol. 16
Classical Machine Learning in Plain English
A cheat sheet for talking with ML specialists in one language: what are data, features, target, loss, generalization and prediction; what problems are solved by linear models, trees, SVM, kNN, ensembles, clustering and PCA; and why the transition to neural networks did not cancel the basic mechanics of errors and optimization, but scaled it.
- Sections
- 7
- Cards
- 31
- 04Vol. 17
Forecasting, Causal Inference & Bandits
The next layer after classic ML. Not just “predict the label”, but understand three different questions: what will happen next, what will happen if we intervene, and what is better to show right now with incomplete information. These are three different formulations of the problem, with different metrics, leakage risks and different language of communication with researchers and product teams.
- Sections
- 7
- Cards
- 32