This video is a recording of a Live Class that focuses on the exam-relevant aspects of the Machine Learning Methods chapter (QTA 14 – Quantitative Analysis, FRM Part 1).

⚠️ Important Note:
Machine learning is a vast and evolving domain. This session is strictly aligned with the FRM Part 1 curriculum, specifically the topics introduced under QTA 4. It is not a general lecture on all machine learning methods. While the concepts here will be useful to anyone interested in ML foundations, please understand that any topic not covered was simply not part of the FRM Part 1 prescribed syllabus.

🎓 In this lecture, we briefly cover:

Philosophical & practical differences between machine learning vs classical econometrics

The logic of training, validation, and test sets and how each is used

Understanding overfitting vs underfitting, their implications, and simple remedies

How to use Principal Component Analysis (PCA) for dimensionality reduction

A walkthrough of K-means clustering, distance measures, inertia, and selecting optimal K

The basics of Natural Language Processing (NLP) and its financial applications

Brief mention of unsupervised, supervised, and reinforcement learning models and their role in decision-making

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