Machine Learning Methods – FRM Part 1-Quantitative Analysis.

Name: Machine Learning Methods – FRM Part 1-Quantitative Analysis.
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Machine learning methods for FRM Part 1: supervised learning, unsupervised learning, PCA, clustering, NLP, reinforcement

Created byMidha Fin

Last updated 6/2026

English

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What you'll learn

Machine learning versus econometrics
Data preparation and rescaling
Training, validation, and testing datasets
Underfitting and overfitting

Course content

1 section • 11 lectures • 1h 43m total length

Introduction1:59
Explore machine learning, a broad set of techniques that let computers learn patterns from data, enabling prediction and classification in finance, from stock trends to fraud detection.
Classical Econometrics and ML14:40
Contrast classical econometrics and machine learning, highlighting theory-driven, interpretable models with hypothesis testing versus data-driven, predictive algorithms that learn from noisy, unstructured data.
Sample splitting and Preparation15:02
Explain how to split data into training, validation, and test sets, tune models with cross-validation and k-fold, and handle time series versus cross-sectional data for robust out-of-sample performance.
The problems of Over-fitting and Under-fitting12:25
Data Preparation7:00
Prepare data by standardizing or normalizing features to a common scale, improving model speed and robustness. Learn unsupervised and supervised methods, including PCA and k-means, with labeled data guiding predictions.
Principal Component Analysis5:35
Learn how principal component analysis reduces a complex data set of correlated indicators to a few uncorrelated factors. Three components explain most yield curve variation, simplifying risk management and hedging.
K-means Algorithm16:24
K-means clustering, an unsupervised method, iteratively updates centroids and assigns points by distance (euclidean or manhattan), using inertia, elbow plots, and silhouette scores to select optimal K.
Natural Language Processing4:49
Reinforcement Learning23:15
Explore how reinforcement learning builds policies to maximize rewards by modeling states, actions, and rewards, and compare Monte Carlo and temporal difference methods with exploration and exploitation strategies.
Difference between Supervised and unsupervised learning0:46
Contrast supervised, unsupervised, and reinforcement learning, noting unsupervised features, supervised prediction and classification, and reinforcement learning by trial and error, with examples like k-means, principal component analysis, lasso, and logistic.
End of Lecture1:52
Assess the weightage of qtf 14 on the final exam, with at least three questions from the last two chapters and a high theory emphasis.

Requirements

Basic understanding of statistics and quantitative analysis
Familiarity with fundamental finance and risk management concepts
No prior machine learning experience required
Suitable for FRM Part 1 candidates and beginners interested in machine learning

Description

Machine Learning Methods is an important topic in modern quantitative finance and risk management, and a key part of the FRM Part 1 Quantitative Analysis syllabus. This course introduces the fundamental concepts of machine learning and explains how these techniques are used to analyze data, identify patterns, and support decision-making in financial markets.

You will learn how machine learning differs from traditional econometric approaches and why it has become increasingly important in finance. The course covers the key stages of model development, including data preparation, variable rescaling, and the use of training, validation, and testing datasets.

The course also explains common challenges in machine learning, such as underfitting and overfitting, and examines techniques used to improve model performance. In addition, you will learn how Principal Component Analysis (PCA) is used for dimensionality reduction, how K-means clustering groups observations into clusters, and how Natural Language Processing (NLP) extracts information from textual data.

Finally, the course introduces the major categories of machine learning models, including supervised learning, unsupervised learning, and reinforcement learning, along with the role of Q-values in reinforcement learning applications.

What this course covers:

Machine learning versus econometrics
Data preparation and rescaling
Training, validation, and testing datasets
Underfitting and overfitting
Principal Component Analysis (PCA)
K-means clustering
Natural Language Processing (NLP)
Supervised, unsupervised, and reinforcement learning
Q-values and reinforcement learning

This course is designed for FRM Part 1 candidates, and is equally useful for risk analysts, finance professionals, and students interested in quantitative finance and data-driven decision-making. The approach is intuitive and exam-focused: concepts are explained clearly before any technical detail, helping learners build a strong conceptual understanding. By the end, you will be able to explain key machine learning concepts, understand how models are developed and evaluated, and appreciate how machine learning techniques are applied in modern finance and risk management.

Who this course is for:

FRM Part 1 candidates preparing for Quantitative Analysis
Risk analysts and risk management professionals
Finance professionals interested in machine learning applications
Students seeking an introduction to machine learning in finance