Machine Learning (2025 Level II CFA® Exam – Quantitative Methods–Module 6)
Learn everything about Machine Learning in the CFA Level II Quantitative Methods module with Professor James Forjan, PhD, CFA. This comprehensive video covers key learning outcome statements (LOS) and real-world examples that help you understand how machine learning applies to finance.
You’ll explore supervised, unsupervised, and deep learning models used in financial analysis, predictive modeling, and portfolio construction.
What You’ll Learn:
Supervised machine learning algorithms (regression, classification, ensemble learning, random forest)
Unsupervised learning (PCA, K-means, hierarchical clustering)
Overfitting and underfitting
Cross-validation techniques (K-fold)
Neural networks, deep learning, and reinforcement learning
Practical financial applications including dividend prediction, credit scoring, and fraud detection
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Topic 1 – Quantitative Methods
Module 6 – Machine Learning
0:00 LOS: Introduction and Learning Outcome Statements
7:25 LOS: Distinguish between supervised machine learning, unsupervised machine learning, and deep learning;
15:40 LOS: Describe over fitting and identify methods of addressing it;
20:54 LOS: Describe supervised machine learning algorithms—including penalized regression, support vector machine, k-nearest neighbor, classification and regression tree, ensemble learning, and random forest—and determine the problems for which they are best suited;
46:17 LOS: Describe unsupervised machine learning algorithms—including principal components analysis, K-means clustering, and hierarchical clustering—and determine problems for which they are best suited;
56:09 LOS: Describe neural networks, deep learning nets, and reinforcement learning.
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