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Curriculum / Phase 3 · Career Pathways

Financial Machine Learning

Study financial machine learning with event labels, finance-aware validation, feature importance and model monitoring.

Why study Financial Machine Learning?

A model can predict a label accurately while adding little economic value. Financial machine learning requires special attention to information timing, overlapping outcomes, evaluation and how a prediction becomes a decision.

Who this is for

For researchers with Python, statistical learning basics and time-ordered financial validation experience.

What you should be able to do

  • Build event-based labels and account for overlapping samples.
  • Evaluate financial models with time-aware validation.
  • Connect feature importance, calibrated probabilities and bet sizing.

An example of the work this prepares you for

A model evaluation pack comparing a baseline and a learned model, with leakage checks, calibration evidence and economic limitations.

Prerequisites and suggested preparation

You should be comfortable with the background covered in these programmes. Equivalent knowledge from university study or independent learning is also suitable.

The goal is to use the foundations in context: explain the assumptions, carry out a calculation and interpret the result. If one of those steps feels unfamiliar, review the linked preparation first.

A preview of the learning journey

The lessons build towards these kinds of reasoning and practical work.

Define a useful target

Connect a prediction task to a financial decision and information set.

Validate honestly

Recognise when sampling or tuning inflates apparent performance.

Interpret model usefulness

Assess stability and economic value alongside predictive metrics.

Members work through the detailed lessons, derivations, worked examples and solutions inside the learning dashboard.

Explore lesson previews by topic

Open a topic preview to see its introduction, learning objectives and prerequisites. Previous and next links let you explore the lessons in study order. Full lessons are available in the member dashboard; selected samples are free to read in full.

Topic previewFinancial labels, weights and memory5 lessonsTopic previewFinance-aware training and validation2 lessonsTopic previewImportance, calibration and sizing3 lessonsTopic previewMonitoring1 lessonTopic previewFoundations: The Learning Problem and Linear Regression6 lessonsTopic previewClassification and Generalized Linear Models6 lessonsTopic previewRegularization and Model Selection4 lessonsTopic previewNonparametric and Tree-Based Methods4 lessonsTopic previewMargin Methods and Kernels6 lessonsTopic previewNeural Networks and Deep Learning7 lessonsTopic previewUnsupervised Learning and Latent Structure7 lessonsTopic previewLearning Theory and Generalisation4 lessonsTopic previewProbabilistic and Graphical Models3 lessonsTopic previewReinforcement Learning3 lessons

Free resources to start learning

Use these alongside the programme or to prepare independently. Start with the introductory material, work through an example, then return to the research papers when the underlying ideas are familiar.

  1. Textbook · Free

    An Introduction to Statistical Learning

    Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani and Jonathan Taylor · Python edition

    Use the free Python textbook for supervised learning, resampling and interpretation.

  2. Video lectures · Free

    Statistical Learning on YouTube

    Stanford · companion lectures

    Watch the companion lectures to develop model intuition.

  3. Research paper · Free

    The Probability of Backtest Overfitting

    David H. Bailey, Jonathan M. Borwein, Marcos López de Prado and Qiji Jim Zhu

    Understand why selection across many trials changes the strength of evidence.

  4. Textbook · Free

    Python for Data Analysis

    Wes McKinney · third edition

    Make data preparation and timing reproducible before training a model.

Links reviewed 14 October 2026. Free refers to the linked reading, notes or videos; printed books and certificates may cost extra.

Find more papers and learning materials in the SelfMadeQuant Directory.

Where this programme fits

  • Systematic Trading and Quantitative Research · required programme
  • Portfolio, Factor and Asset-Management Quant · elective programme

Ready to study Financial Machine Learning? Explore membership for access to the complete lessons.

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